<p>The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is responsible for accelerating the firm’s data and analytics journey. This includes ensuring the quality, integrity, and security of the company’s data, as well as leveraging this data to generate insights and drive decision-making. The CDAO is also responsible for developing and implementing solutions that support the firm’s commercial goals by harnessing artificial intelligence and machine learning technologies to develop new products, improve productivity, and enhance risk management effectively and responsibly.</p>
<p>As an Executive Director on the Machine Learning Center of Excellence (MLCOE) team, you will be responsible for applying advanced machine learning techniques to a variety of complex tasks. These tasks include natural language processing, speech analytics, time series, reinforcement learning, and recommendation systems. You will work closely with different teams and actively contribute to our knowledge sharing community.</p>
<p>In a highly collaborative environment, you will partner with business professionals, technologists, and control partners to implement solutions into production. Your passion for machine learning should be evident, and you should be willing to dedicate time to learning, researching, and experimenting with new innovations in the field. Your expertise in Deep Learning and Large Language Models, coupled with hands-on implementation experience, strong analytical thinking, a deep desire to learn, and high motivation, will be crucial for this role.</p>
<h3>Job Responsibilities</h3>
<ol>
<li>Research and explore new machine learning methods through independent study, attending industry-leading conferences, experimentation and participating in our knowledge sharing community.</li>
<li>Develop state-of-the art machine learning models to solve real-world problems and apply it to tasks such as natural language processing (NLP), speech recognition and analytics, time-series predictions or recommendation systems.</li>
<li>Collaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into production.</li>
<li>Drive Firm wide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across different areas of the business.</li>
</ol>
<h3>Required Qualifications, Capabilities, and Skills</h3>
<ol>
<li>PhD in a quantitative discipline, e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science, with at least five years of industry or research experience in the field. Or an MS with at least eight years of experience.</li>
<li>Solid background in NLP or speech recognition and analytics, personalization/recommendation and hands-on experience and solid understanding of machine learning and deep learning methods.</li>
<li>Extensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas).</li>
<li>Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals.</li>
<li>Experience with big data and scalable model training and solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences.</li>
<li>Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments.</li>
<li>Curious, hardworking and detail-oriented, and motivated by complex analytical problems.</li>
</ol>
<h3>Preferred Qualifications, Capabilities, and Skills</h3>
<ol>
<li>Strong background in Mathematics and Statistics and familiarity with the financial services industries and continuous integration models and unit test development.</li>
<li>Knowledge in search/ranking, Reinforcement Learning or Meta Learning.</li>
<li>Experience with A/B experimentation and data/metric-driven product development, cloud-native deployment in a large scale distributed environment and ability to develop and debug production-quality code.</li>
<li>Strong publication record in Natural Language Processing, Machine Learning, Deep Learning or Reinforcement Learning at major conferences or journals.</li>
</ol>
<p>JPMorgan Chase & Co., one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.</p>
<p>We offer a competitive total rewards package including base salary determined based on the role, experience, skill set, and location. For those in eligible roles, we offer discretionary incentive compensation which may be awarded in recognition of firm performance and individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.</p>
<p>We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs.</p>
<p>JPMorgan Chase is an Equal Opportunity Employer, including Disability/Veterans.</p>
<h3>Company:</h3>
<p>JPMorgan Chase & Co.</p>
<h3>Qualifications:</h3>
<h3>Language requirements:</h3>
<h3>Specific requirements:</h3>
<h3>Educational level:</h3>
<h3>Level of experience (years):</h3>
<p>Senior (5+ years of experience)</p>
#J-18808-Ljbffr

<p>About Arc Institute
The Arc Institute is a new scientific institution conducting curiosity-driven basic science and technology development to understand and treat complex human diseases. Headquartered in Palo Alto, California, Arc is an independent research organization founded on the belief that many important research programs will be enabled by new institutional models. Arc operates in partnership with Stanford University, UCSF, and UC Berkeley.
While the prevailing university research model has yielded many tremendous successes, we believe in the importance of institutional experimentation as a way to make progress. These include:

Funding: Arc fully funds Core Investigators’ (PIs’) research groups, liberating scientists from the typical constraints of project-based external grants.
Technology: Biomedical research has become increasingly dependent on complex tooling. Arc Technology Centers develop, optimize, and deploy rapidly advancing experimental and computational technologies in collaboration with Core Investigators.
Support: Arc aims to provide first-class support—operationally, financially, and scientifically—that will enable scientists to pursue long-term high risk, high reward research that can meaningfully advance progress in disease cures, including neurodegeneration, cancer, and immune dysfunction.
Culture: We believe that culture matters enormously in science and that excellence is difficult to sustain. We aim to create a culture that is focused on scientific curiosity, a deep commitment to truth, broad ambition, and selfless collaboration.
Arc has scaled to nearly 200 people to date. With $650M+ in committed funding and a state of the art new lab facility in Palo Alto, Arc will continue to grow quickly in the coming years.
About The Position
The Arc Institute is seeking a Machine Learning Scientist to join the Hsu and Konermann Labs at the Arc Institute (also affiliated with UC Berkeley Bioengineering and Stanford Biochemistry). The successful candidate will play a crucial role in advancing the state-of-the-art in generative AI applied to biology, including our frontier DNA foundation model (Evo). You will focus on developing ML models for biological data, leveraging frontier approaches like hybrid masked-causal objectives, discrete diffusion, mechanistic interpretability techniques, and more. You will also apply your models for important computational biology applications in genome mining, molecular technology development, and invention of new therapeutic approaches.
This role is a unique opportunity to advance state-of-the-art machine learning in genomics, contribute to high-impact scientific discoveries, and help define how computational approaches shape our understanding and engineering of biology. You will work in a highly collaborative team with expert experimental biologists to realize the full impact of your work, and also have the opportunity to contribute to Institute-wide machine learning efforts such as Arc’s Virtual Cell Initiative.
About You
You are passionate about developing machine learning models with real-world applications and scientific impact.
You have a strong understanding of modern deep learning, computational biology, and genetics.
You are excited about collaborating with a multidisciplinary team of experimental biologists and machine learning researchers at Arc.
You are known for your ability to analyze/visualize complex datasets, build high-quality ML tools, draw meaningful conclusions, and work effectively in a multidisciplinary team.
You are familiar with recently reported projects in the lab, including our recent work in the fields of generative genomics and biological design: genomic language modeling, protein language modeling, and discovering new genome engineering tools.
In this position you will
Train and evaluate state-of-the-art machine learning models for molecular biology and genomics.
Develop methods for applying biological language models to the study of both eukaryotic and prokaryotic genome function and gene discovery.
Apply recent advances in mechanistic interpretability, such as sparse autoencoders, to the study of genomic language models such as Evo.
Develop, test, and maintain modular open-source software to accelerate adoption and application of genomic language models.
Contribute directly to ongoing discovery projects in the lab in the fields of generative genomics, genome engineering, and therapeutics.
Effectively communicate analysis results to experimental scientists as well as computational scientists.
Requirements
Ph.D. in Computer Science, Bioinformatics, Computational Biology, Genetics/Genomics, with 0-5 years of industry/academia experience post degree.
Hands-on experience with training and evaluating the performance of machine learning models for large datasets.
High competency with Python, bash, and standard deep learning frameworks such as PyTorch.
Experience with Linux, git/GitHub, Docker, and Jupyter/RMarkdown notebooks.
Appreciation for how choices in experimental design affect the data analysis process.
Enjoy working collaboratively and cross-functionally with experimental scientists.

#J-18808-Ljbffr</p>

<p>The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is responsible for accelerating the firm’s data and analytics journey. This includes ensuring the quality, integrity, and security of the company’s data, as well as leveraging this data to generate insights and drive decision-making. The CDAO is also responsible for developing and implementing solutions that support the firm’s commercial goals by harnessing artificial intelligence and machine learning technologies to develop new products, improve productivity, and enhance risk management effectively and responsibly. </p><p>As an Executive Director on the Machine Learning Center of Excellence (MLCOE) team, you will be responsible for applying advanced machine learning techniques to a variety of complex tasks. These tasks include natural language processing, speech analytics, time series, reinforcement learning, and recommendation systems. You will work closely with different teams and actively contribute to our knowledge sharing community. </p><p>In a highly collaborative environment, you will partner with business professionals, technologists, and control partners to implement solutions into production. Your passion for machine learning should be evident, and you should be willing to dedicate time to learning, researching, and experimenting with new innovations in the field. Your expertise in Deep Learning and Large Language Models, coupled with hands-on implementation experience, strong analytical thinking, a deep desire to learn, and high motivation, will be crucial for this role. </p><h3>Job Responsibilities </h3><ol><li>Research and explore new machine learning methods through independent study, attending industry-leading conferences, experimentation and participating in our knowledge sharing community. </li><li>Develop state-of-the art machine learning models to solve real-world problems and apply it to tasks such as natural language processing (NLP), speech recognition and analytics, time-series predictions or recommendation systems. </li><li>Collaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into production. </li><li>Drive Firm wide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across different areas of the business. </li> </ol><h3>Required Qualifications, Capabilities, and Skills </h3><ol><li>PhD in a quantitative discipline, e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science, with at least five years of industry or research experience in the field. Or an MS with at least eight years of experience. </li><li>Solid background in NLP or speech recognition and analytics, personalization/recommendation and hands-on experience and solid understanding of machine learning and deep learning methods. </li><li>Extensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas). </li><li>Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals. </li><li>Experience with big data and scalable model training and solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences. </li><li>Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments. </li><li>Curious, hardworking and detail-oriented, and motivated by complex analytical problems. </li> </ol><h3>Preferred Qualifications, Capabilities, and Skills </h3><ol><li>Strong background in Mathematics and Statistics and familiarity with the financial services industries and continuous integration models and unit test development. </li><li>Knowledge in search/ranking, Reinforcement Learning or Meta Learning. </li><li>Experience with A/B experimentation and data/metric-driven product development, cloud-native deployment in a large scale distributed environment and ability to develop and debug production-quality code. </li><li>Strong publication record in Natural Language Processing, Machine Learning, Deep Learning or Reinforcement Learning at major conferences or journals. </li> </ol><p>JPMorgan Chase & Co., one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management. </p><p>We offer a competitive total rewards package including base salary determined based on the role, experience, skill set, and location. For those in eligible roles, we offer discretionary incentive compensation which may be awarded in recognition of firm performance and individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process. </p><p>We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. </p><p>JPMorgan Chase is an Equal Opportunity Employer, including Disability/Veterans. </p><h3>Company: </h3><p>JPMorgan Chase & Co. </p><h3>Qualifications: </h3><h3>Language requirements: </h3><h3>Specific requirements: </h3><h3>Educational level: </h3><h3>Level of experience (years): </h3><p>Senior (5+ years of experience) </p>#J-18808-Ljbffr

<p>About Arc Institute </p><p>The Arc Institute is a new scientific institution conducting curiosity-driven basic science and technology development to understand and treat complex human diseases. Headquartered in Palo Alto, California, Arc is an independent research organization founded on the belief that many important research programs will be enabled by new institutional models. Arc operates in partnership with Stanford University, UCSF, and UC Berkeley. </p><p>While the prevailing university research model has yielded many tremendous successes, we believe in the importance of institutional experimentation as a way to make progress. These include:<br/> </p><ol><li><b>Funding: </b> Arc fully funds Core Investigators’ (PIs’) research groups, liberating scientists from the typical constraints of project-based external grants. </li><li><b>Technology: </b> Biomedical research has become increasingly dependent on complex tooling. Arc Technology Centers develop, optimize, and deploy rapidly advancing experimental and computational technologies in collaboration with Core Investigators. </li><li><b>Support: </b> Arc aims to provide first-class support—operationally, financially, and scientifically—that will enable scientists to pursue long-term high risk, high reward research that can meaningfully advance progress in disease cures, including neurodegeneration, cancer, and immune dysfunction. </li><li><b>Culture: </b> We believe that culture matters enormously in science and that excellence is difficult to sustain. We aim to create a culture that is focused on scientific curiosity, a deep commitment to truth, broad ambition, and selfless collaboration. </li> </ol><p>Arc has scaled to nearly 200 people to date. With $650M+ in committed funding and a state of the art new lab facility in Palo Alto, Arc will continue to grow quickly in the coming years. </p><p>About The Position </p><p>The Arc Institute is seeking a Machine Learning Scientist to join the Hsu and Konermann Labs at the Arc Institute (also affiliated with UC Berkeley Bioengineering and Stanford Biochemistry). The successful candidate will play a crucial role in advancing the state-of-the-art in generative AI applied to biology, including our frontier DNA foundation model (Evo). You will focus on developing ML models for biological data, leveraging frontier approaches like hybrid masked-causal objectives, discrete diffusion, mechanistic interpretability techniques, and more. You will also apply your models for important computational biology applications in genome mining, molecular technology development, and invention of new therapeutic approaches. </p><p>This role is a unique opportunity to advance state-of-the-art machine learning in genomics, contribute to high-impact scientific discoveries, and help define how computational approaches shape our understanding and engineering of biology. You will work in a highly collaborative team with expert experimental biologists to realize the full impact of your work, and also have the opportunity to contribute to Institute-wide machine learning efforts such as Arc’s Virtual Cell Initiative. </p><p>About You </p><ul><li>You are passionate about developing machine learning models with real-world applications and scientific impact. </li><li>You have a strong understanding of modern deep learning, computational biology, and genetics. </li><li>You are excited about collaborating with a multidisciplinary team of experimental biologists and machine learning researchers at Arc. </li><li>You are known for your ability to analyze/visualize complex datasets, build high-quality ML tools, draw meaningful conclusions, and work effectively in a multidisciplinary team. </li><li>You are familiar with recently reported projects in the lab, including our recent work in the fields of generative genomics and biological design: genomic language modeling, protein language modeling, and discovering new genome engineering tools. </li> </ul><p>In this position you will </p><ol><li>Train and evaluate state-of-the-art machine learning models for molecular biology and genomics. </li><li>Develop methods for applying biological language models to the study of both eukaryotic and prokaryotic genome function and gene discovery. </li><li>Apply recent advances in mechanistic interpretability, such as sparse autoencoders, to the study of genomic language models such as Evo. </li><li>Develop, test, and maintain modular open-source software to accelerate adoption and application of genomic language models. </li><li>Contribute directly to ongoing discovery projects in the lab in the fields of generative genomics, genome engineering, and therapeutics. </li><li>Effectively communicate analysis results to experimental scientists as well as computational scientists. </li> </ol><p>Requirements </p><ul><li>Ph.D. in Computer Science, Bioinformatics, Computational Biology, Genetics/Genomics, with 0-5 years of industry/academia experience post degree. </li><li>Hands-on experience with training and evaluating the performance of machine learning models for large datasets. </li><li>High competency with Python, bash, and standard deep learning frameworks such as PyTorch. </li><li>Experience with Linux, git/GitHub, Docker, and Jupyter/RMarkdown notebooks. </li><li>Appreciation for how choices in experimental design affect the data analysis process. </li><li>Enjoy working collaboratively and cross-functionally with experimental scientists. </li> </ul>#J-18808-Ljbffr

About Arc Institute

The Arc Institute is a new scientific institution conducting curiosity-driven basic science and technology development to understand and treat complex human diseases. Headquartered in Palo Alto, California, Arc is an independent research organization founded on the belief that many important research programs will be enabled by new institutional models. Arc operates in partnership with Stanford University, UCSF, and UC Berkeley.

While the prevailing university research model has yielded many tremendous successes, we believe in the importance of institutional experimentation as a way to make progress. These include:
Funding:  Arc fully funds Core Investigators' (PIs') research groups, liberating scientists from the typical constraints of project-based external grants.
Technology:  Biomedical research has become increasingly dependent on complex tooling. Arc Technology Centers develop, optimize, and deploy rapidly advancing experimental and computational technologies in collaboration with Core Investigators.
Support:  Arc aims to provide first-class support-operationally, financially, and scientifically-that will enable scientists to pursue long-term high risk, high reward research that can meaningfully advance progress in disease cures, including neurodegeneration, cancer, and immune dysfunction.
Culture:  We believe that culture matters enormously in science and that excellence is difficult to sustain. We aim to create a culture that is focused on scientific curiosity, a deep commitment to truth, broad ambition, and selfless collaboration.
Arc has scaled to nearly 200 people to date. With $650M+ in committed funding and a state of the art new lab facility in Palo Alto, Arc will continue to grow quickly in the coming years.

About the position

The Arc Institute is seeking a Machine Learning Scientist to join the Hsu and Konermann Labs at the Arc Institute (also affiliated with UC Berkeley Bioengineering and Stanford Biochemistry). The successful candidate will play a crucial role in advancing the state-of-the-art in generative AI applied to biology, including our frontier DNA foundation model (Evo). You will focus on developing ML models for biological data, leveraging frontier approaches like hybrid masked-causal objectives, discrete diffusion, mechanistic interpretability techniques, and more. You will also apply your models for important computational biology applications in genome mining, molecular technology development, and invention of new therapeutic approaches.

This role is a unique opportunity to advance state-of-the-art machine learning in genomics, contribute to high-impact scientific discoveries, and help define how computational approaches shape our understanding and engineering of biology. You will work in a highly collaborative team with expert experimental biologists to realize the full impact of your work, and also have the opportunity to contribute to Institute-wide machine learning efforts such as Arc's Virtual Cell Initiative.

About you     You are passionate about developing machine learning models with real-world applications and scientific impact
You have a strong understanding of modern deep learning, computational biology, and genetics
You are excited about collaborating with a multidisciplinary team of experimental biologists and machine learning researchers at Arc
You are known for your ability to analyze/visualize complex datasets, build high-quality ML tools, draw meaningful conclusions, and work effectively in a multidisciplinary team.
You are familiar with recently reported projects in the lab, including our recent work in the fields of generative genomics and biological design: genomic language modeling, protein language modeling, and discovering new genome engineering tools
In this position you will     Train and evaluate state-of-the-art machine learning models for molecular biology and genomics
Develop methods for applying biological language models to the study of both eukaryotic and prokaryotic genome function and gene discovery
Apply recent advances in mechanistic interpretability, such as sparse autoencoders, to the study of genomic language models such as Evo
Develop, test, and maintain modular open-source software to accelerate adoption and application of genomic language models.
Contribute directly to ongoing discovery projects in the lab in the fields of generative genomics, genome engineering, and therapeutics
Effectively communicate analysis results to experimental scientists as well as computational scientists
Requirements     Ph.D. in Computer Science, Bioinformatics, Computational Biology, Genetics/Genomics, with 0-5 years of industry/academia experience post degree.
Hands-on experience with training and evaluating the performance of machine learning models for large datasets
High competency with Python, bash, and standard deep learning frameworks such as PyTorch
Experience with Linux, git/GitHub, Docker, and Jupyter/RMarkdown notebooks.
Appreciation for how choices in experimental design affect the data analysis process.
Enjoy working collaboratively and cross-functionally with experimental scientists

The base salary range for this position is $124,000 to $148,000. These amounts reflect the range of base salary that the Institute reasonably would expect to pay a new hire or internal candidate for this position. The actual base compensation paid to any individual for this position may vary depending on factors such as experience, market conditions, education/training, skill level, and whether the compensation is internally equitable, and does not include bonuses, commissions, differential pay, other forms of compensation, or benefits. This position is also eligible to receive an annual discretionary bonus, with the amount dependent on individual and institute performance factors.

<p>The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is responsible for accelerating the firm’s data and analytics journey. This includes ensuring the quality, integrity, and security of the company’s data, as well as leveraging this data to generate insights and drive decision-making. The CDAO is also responsible for developing and implementing solutions that support the firm’s commercial goals by harnessing artificial intelligence and machine learning technologies to develop new products, improve productivity, and enhance risk management effectively and responsibly.

As an Executive Director on the Machine Learning Center of Excellence (MLCOE) team, you will be responsible for applying advanced machine learning techniques to a variety of complex tasks. These tasks include natural language processing, speech analytics, time series, reinforcement learning, and recommendation systems. You will work closely with different teams and actively contribute to our knowledge sharing community.

In a highly collaborative environment, you will partner with business professionals, technologists, and control partners to implement solutions into production. Your passion for machine learning should be evident, and you should be willing to dedicate time to learning, researching, and experimenting with new innovations in the field. Your expertise in Deep Learning and Large Language Models, coupled with hands-on implementation experience, strong analytical thinking, a deep desire to learn, and high motivation, will be crucial for this role.

Job Responsibilities

Research and explore new machine learning methods through independent study, attending industry-leading conferences, experimentation and participating in our knowledge sharing community.

Develop state-of-the art machine learning models to solve real-world problems and apply it to tasks such as natural language processing (NLP), speech recognition and analytics, time-series predictions or recommendation systems.

Collaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into production.

Drive Firm wide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across different areas of the business.

Required Qualifications, Capabilities, and Skills

PhD in a quantitative discipline, e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science, with at least five years of industry or research experience in the field. Or an MS with at least eight years of experience.

Solid background in NLP or speech recognition and analytics, personalization/recommendation and hands-on experience and solid understanding of machine learning and deep learning methods.

Extensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas).

Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals.

Experience with big data and scalable model training and solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences.

Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments.

Curious, hardworking and detail-oriented, and motivated by complex analytical problems.

Preferred Qualifications, Capabilities, and Skills

Strong background in Mathematics and Statistics and familiarity with the financial services industries and continuous integration models and unit test development.

Knowledge in search/ranking, Reinforcement Learning or Meta Learning.

Experience with A/B experimentation and data/metric-driven product development, cloud-native deployment in a large scale distributed environment and ability to develop and debug production-quality code.

Strong publication record in Natural Language Processing, Machine Learning, Deep Learning or Reinforcement Learning at major conferences or journals.

JPMorgan Chase & Co., one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.

We offer a competitive total rewards package including base salary determined based on the role, experience, skill set, and location. For those in eligible roles, we offer discretionary incentive compensation which may be awarded in recognition of firm performance and individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs.

JPMorgan Chase is an Equal Opportunity Employer, including Disability/Veterans.

Company:
JPMorgan Chase & Co.

Qualifications:
Language requirements:
Specific requirements:
Educational level:
Level of experience (years):
Senior (5+ years of experience)

#J-18808-Ljbffr</p>

Machine Learning Scientist Jobs in Palo Alto, CA

Machine Learning Scientist Natural Language Processing Nlp Executive Director
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Machine Learning Scientist Hsu Konermann Lab
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Machine Learning Scientist Hsu Konermann Lab
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Machine Learning Scientist Hsu Konermann Lab
December 6, 2024

Machine Learning Scientist Natural Language Processing Nlp Executive Director
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Machine Learning Scientist Jobs in Palo Alto, CA

Machine Learning Scientist Natural Language Processing Nlp Executive DirectorDecember 11, 2024

Machine Learning Scientist Hsu Konermann LabDecember 10, 2024

Machine Learning Scientist Natural Language Processing Nlp Executive DirectorDecember 9, 2024

Machine Learning Scientist Hsu Konermann LabDecember 9, 2024

Machine Learning Scientist Hsu Konermann LabDecember 6, 2024

Machine Learning Scientist Natural Language Processing Nlp Executive DirectorNovember 29, 2024