<h3>Minimum Requirements </h3><ul><li>BS degree or higher in Electrical Engineering, Software Engineering, Computer Science, Machine Learning, Data Science, or related field </li><li>Minimum 2 years of experience working with some of the following: A/V conferencing products, video products, professional audio products, live sound, live video production, or other related subject matter </li><li>Strong proficiency with scripting languages such as Python </li><li>Good verbal and written communication skills </li> </ul><h3>Responsibilities </h3><ul><li>In this role, you'll get to flex your experimental muscles by designing and implementing experiments to validate new video and audio signal processing designs </li><li>Your genius will be essential in outlining and executing strategies that will take our products to the next level </li><li>Work collaboratively with data scientists, machine learning engineers, and DSP engineers to create, execute, and execute test plans, both in simulation and as implemented on embedded products, to identify areas of improvement and validate expected functionality of video and audio signal processing, machine learning, and artificial intelligence </li><li>Identify test strategies that further elucidate the relationships between system performance and system input </li><li>Develop regression tests and possibly subjective listening tests and visual tests whose execution and results are understandable and repeatable by other team members </li><li>Implement python scripts to automate tests </li><li>Work collaboratively to objectively automate algorithm parameter tuning for product development </li><li>Make data-driven recommendations to developers for areas of improvement of the algorithms, where applicable </li><li>Outline and execute strategies to record, curate, and utilize behavioral audio databases for algorithm development and machine learning </li><li>Willing to wear multiple hats as needed on a small team with independence and an openness to collaborate to solve problems as they are discovered </li><li>When applicable, test and analyze competitive systems, document results, and provide differential analyses </li><li>Document results, organize outputs, and effectively communicate results to team members </li><li>Perform other duties as assigned </li> </ul><h3>Who We Are </h3><p>Shure’s mission is to be the most trusted audio brand worldwide – and for nearly a century, our Core Values have aligned us to be just that. Founded in 1925, we are a leading global manufacturer of audio equipment known for quality, reliability, and durability. We engineer microphones, headphones, wireless audio systems, conferencing systems, and more. And quality doesn’t stop at our products. Our talented teams strive for perfection and innovate every chance they get. We offer an Associate-first culture, flexible work arrangements, and opportunity for all. </p><p>Shure Incorporated is headquartered in Niles, Illinois, with remote and hybrid opportunities throughout the United States. We have more than 35 regional sales offices, engineering hubs, and manufacturing facilities throughout the Americas, EMEA, and Asia. </p><p>THE MIX MATTERS </p><p>Don’t check off every box in the job requirements? No problem! We recognize that every professional journey is unique and are committed to providing an equitable candidate experience for all prospective Shure Associates. If you’re excited about this role, believe you’ve got the skills to be successful, and share our passion for creating an inclusive, diverse, equitable, and accessible work environment, then apply. </p>#J-18808-Ljbffr

Center 1 (19052), United States of America, McLean, VirginiaLead Machine Learning Engineer (Remote-Eligible)As a Capital One Machine Learning Engineer (MLE), you''ll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You''ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You''ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You''ll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. What you''ll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you''ll be expected to perform many ML engineering activities, including one or more of the following:Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation).Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. Retrain, maintain, and monitor models in production.Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale.Construct optimized data pipelines to feed ML models. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. Use programming languages like Python, Scala, or Java. Capital One is open to hiring a Remote Employee for this opportunityBasic Qualifications:Bachelor''s degree At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)At least 4 years of experience programming with Python, Scala, or JavaAt least 2 years of experience building, scaling, and optimizing ML systemsPreferred Qualifications:Master''s or doctoral degree in computer science, electrical engineering, mathematics, or a similar field3+ years of experience building production-ready data pipelines that feed ML models 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow 2+ years of experience developing performant, resilient, and maintainable code2+ years of experience with data gathering and preparation for ML models2+ years of people leader experience1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud PlatformExperience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents At this time, Capital One will not sponsor a new applicant for employment authorization for this position.No agencies please. Capital One is an Equal Opportunity Employer committed to diversity and inclusion in the workplace. All qualified applicants will receive consideration for employment without regard to sex, race, color, age, national origin, religion, physical and mental disability, genetic information, marital status, sexual orientation, gender identity/assignment, citizenship, pregnancy or maternity, protected veteran status, or any other status prohibited by applicable national, federal, state or local law. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City''s Fair Chance Act; Philadelphia''s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at [Click Here to Email Your Resum]. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.For technical support or questions about Capital One''s recruiting process, please send an email to [Click Here to Email Your Resum]Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

<p>Center 1 (19052), United States of America, McLean, VirginiaLead Machine Learning Engineer (Remote-Eligible)As a Capital One Machine Learning Engineer (MLE), you''ll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You''ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You''ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You''ll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. What you''ll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you''ll be expected to perform many ML engineering activities, including one or more of the following:Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation).Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. Retrain, maintain, and monitor models in production.Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale.Construct optimized data pipelines to feed ML models. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. Use programming languages like Python, Scala, or Java. Capital One is open to hiring a Remote Employee for this opportunityBasic Qualifications:Bachelor''s degree At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)At least 4 years of experience programming with Python, Scala, or JavaAt least 2 years of experience building, scaling, and optimizing ML systemsPreferred Qualifications:Master''s or doctoral degree in computer science, electrical engineering, mathematics, or a similar field3+ years of experience building production-ready data pipelines that feed ML models 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow 2+ years of experience developing performant, resilient, and maintainable code2+ years of experience with data gathering and preparation for ML models2+ years of people leader experience1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud PlatformExperience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents At this time, Capital One will not sponsor a new applicant for employment authorization for this position.No agencies please. Capital One is an Equal Opportunity Employer committed to diversity and inclusion in the workplace. All qualified applicants will receive consideration for employment without regard to sex, race, color, age, national origin, religion, physical and mental disability, genetic information, marital status, sexual orientation, gender identity/assignment, citizenship, pregnancy or maternity, protected veteran status, or any other status prohibited by applicable national, federal, state or local law. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City''s Fair Chance Act; Philadelphia''s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at [Click Here to Email Your Resum]. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.For technical support or questions about Capital One''s recruiting process, please send an email to [Click Here to Email Your Resum]Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).</p>

Machine Learning Engineer Jobs in Niles, IL

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December 9, 2024

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December 2, 2024

Lead Machine Learning Engineer Remoteeligible
November 30, 2024

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Machine Learning Engineer Jobs in Niles, IL

Machine Learning Engineer Ii ValidationDecember 9, 2024

Lead Machine Learning Engineer RemoteeligibleDecember 2, 2024

Lead Machine Learning Engineer RemoteeligibleNovember 30, 2024

Quick filters

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Machine Learning Engineer Ii Validation
December 9, 2024

Lead Machine Learning Engineer Remoteeligible
December 2, 2024

Lead Machine Learning Engineer Remoteeligible
November 30, 2024