Cohere Health is illuminating healthcare for patients, their doctors, and everyone significant in a patient's healthcare experience. Founded in August 2019, the company focuses on eliminating unnecessary friction in healthcare that is unrelated to health and treatment. They build software to ensure the appropriate care plan is understood and quickly approved, allowing patients and doctors to concentrate on health instead of administrative hassles.

In this role, you will join a team of world-class machine learning engineers and clinical experts to deploy models that automate burdensome administrative practices. You’ll work on designing, deploying, and monitoring production models that predict and generate relevant clinical findings from various data sources. Candidates should have a strong background in machine learning, Python, and deep learning frameworks. If you align with their core values of empathy, kindness, and inclusivity, Cohere Health could be the perfect place for you.

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Cohere Health as a Machine Learning Engineer. Whether you were contacted by a Cohere Health recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Cohere Health Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the Cohere Health Machine Learning Engineer hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the Cohere Health Machine Learning Engineer role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around the design, deployment, and monitoring of production machine learning models, particularly focusing on deep learning approaches such as transformers.

In the case of machine learning roles, take-home assignments regarding machine learning model building, assessment, and deployment may be incorporated. Apart from these, your proficiency in Python and experience with deep learning frameworks like PyTorch may also be assessed during the round.

Depending on the seniority of the position, real-scenario problems related to clinical and unstructured data may also be assigned.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds, varying with the role, will be conducted during your day at the Cohere Health office. Your technical prowess, including programming and ML modeling capabilities, will be evaluated against the finalized candidates throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the Machine Learning Engineer role at Cohere Health.

Quick Tips For Cohere Health Machine Learning Engineer Interviews

A few tips for acing your Cohere Health interview include:

• Understand Clinical Context: Cohere Health focuses on automating clinical administrative practices using machine learning. Brush up on basic clinical concepts and how they apply to machine learning models.
• Emphasize Deep Learning Proficiency: Given the focus on transformers and deep learning for NLP tasks, demonstrate your expertise in frameworks like PyTorch and deep learning applications during technical discussions.
• Highlight Cross-Functional Collaboration: Work at Cohere Health involves multiple stakeholders, including clinicians and product managers. Showcasing your experience working in diverse, cross-functional teams will be advantageous.

Typically, interviews at Cohere Health vary by role and team, but commonly Machine Learning Engineer interviews follow a fairly standardized process across these question topics.

• Write a SQL query to select the 2nd highest salary in the engineering department. Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

• Write a function to find the maximum number in a list of integers. Given a list of integers, write a function that returns the maximum number in the list. If the list is empty, return None.

• Create a function convert_to_bst to convert a sorted list into a balanced binary tree. Given a sorted list, create a function convert_to_bst that converts the list into a balanced binary tree. The output binary tree should be balanced, meaning the height difference between the left and right subtree of all the nodes should be at most one.

• Write a function to simulate drawing balls from a jar. Write a function to simulate drawing balls from a jar. The colors of the balls are stored in a list named jar, with corresponding counts of the balls stored in the same index in a list called n_balls.

• Develop a function can_shift to determine if one string can be shifted to become another. Given two strings A and B, write a function can_shift to return whether or not A can be shifted some number of places to get B.

• What are the drawbacks of having student test scores organized in the given layouts? Assume you have data on student test scores in two different layouts. Identify the drawbacks of these layouts and suggest formatting changes to make the data more useful for analysis. Additionally, describe common problems seen in "messy" datasets.

• How would you locate a mouse in a 4x4 grid using the fewest scans? You have a 4x4 grid with a mouse trapped in one of the cells. You can scan subsets of cells to know if the mouse is within that subset. Describe a strategy to find the mouse using the fewest number of scans.

• How would you select Dashers for Doordash deliveries in NYC and Charlotte? Doordash is launching delivery services in New York City and Charlotte. Describe the process for selecting dashers (delivery drivers) and discuss whether the criteria for selection should be the same for both cities.

• What factors could bias Jetco's study on boarding times? Jetco, a new airline, was found to have the fastest average boarding times in a study. Identify potential factors that could have biased this result and what you would investigate further.

• How would you design an A/B test to evaluate a pricing increase for a B2B SAAS company? You work at a B2B SAAS company interested in testing different subscription pricing levels. Describe how you would design a two-week-long A/B test to evaluate a pricing increase and determine if it is a good business decision.

• How much should we budget for a $5 coupon initiative in a ride-sharing app? A ride-sharing app has a probability (p) of dispensing a $5 coupon to a rider and services (N) riders. Calculate the total budget needed for the coupon initiative.

• What is the probability of riders getting a coupon in a ride-sharing app? A driver using the app picks up two passengers. Determine:

• The probability of both riders getting the coupon.

• The probability that only one of them will get the coupon.

• What is a confidence interval for a statistic and why is it useful? Explain what a confidence interval is, why it is useful to know the confidence interval for a statistic, and how to calculate it.

• What is the probability of finding an item on Amazon's website given warehouse availability? Amazon has a warehouse system where item X is available at warehouse A with a probability of 0.6 and at warehouse B with a probability of 0.8. Calculate the probability that item X would be found on Amazon's website.

• Is a coin fair if it comes up tails 8 times out of 10 flips? You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if this is a fair coin.

• What are time series models and why are they needed? Describe what time series models are and explain why they are necessary when less complicated regression models exist.

• How would you justify the complexity of building a neural network model and explain predictions to non-technical stakeholders? Your manager asks you to build a neural network model to solve a business problem. How would you justify the complexity of the model and explain its predictions to non-technical stakeholders?

• How would you evaluate and deploy a decision tree model for predicting loan repayment? You are tasked with building a decision tree model to predict if a borrower will repay a personal loan. How would you evaluate if a decision tree is the correct model? How would you evaluate its performance before and after deployment?

• How does random forest generate the forest, and why use it over logistic regression? Explain how random forest generates its forest. Additionally, why would you choose random forest over other algorithms like logistic regression?

• How would you explain linear regression to a child, a college student, and a mathematician? Explain the concept of linear regression to three different audiences: a child, a first-year college student, and a seasoned mathematician. Tailor your explanations to each audience's understanding level.

• What are the key differences between classification models and regression models? Describe the main differences between classification models and regression models.

What is the average salary for a Machine Learning Engineer at Cohere Health?

Q: What is the mission of Cohere Health?

Cohere Health aims to illuminate healthcare for patients, their doctors, and everyone involved in a patient's healthcare experience, both inside and outside the doctor's office. They focus on eliminating wasteful friction that distracts patients and doctors from health and treatment, particularly for costly diagnoses requiring expensive procedures or medications.

Q: What kind of work does the Machine Learning Engineer position involve at Cohere Health?

In this role, you'd be designing, deploying, and monitoring production models to automate burdensome administrative clinical practices. You'll work with both structured and unstructured data to extract, predict, and generate relevant clinical findings.

Q: What skills and experience are required for the Machine Learning Engineer position at Cohere Health?

Candidates should have at least 1-3 years of experience in applied machine learning, hold a degree in a relevant field like computer science or mathematics, and be comfortable with Python and deep learning frameworks like PyTorch. Hands-on experience with deep learning models such as transformers for NLP tasks is crucial.

Q: What makes Cohere Health a unique place to work?

Cohere Health is deeply invested in maintaining a supportive, growth-oriented environment with a diverse, inclusive team. People who succeed here are empathetic teammates, candid, kind, and embody Cohere's core values and principles. The company believes that diverse teams create the most impactful work.

Q: How can I prepare for an interview at Cohere Health?

To prepare for an interview at Cohere Health, research the company, understand their mission, and review your technical skills, especially in deep learning, Python, and PyTorch. Practicing with Interview Query could be particularly beneficial to ensure you're ready for technical and behavioral questions related to the role.

Embark on an exhilarating journey with Cohere Health, where you will play a pivotal role in transforming healthcare through state-of-the-art machine learning. If you're a passionate machine learning engineer with a heart for innovation and a drive to make a real-world impact, then this is your chance to join a dynamic team dedicated to reducing friction in healthcare workflows.

To learn more about what it takes to excel in the Machine Learning Engineer interview at Cohere Health, check out our comprehensive Cohere Health Interview Guide. We've meticulously curated interview questions and insights to arm you with the confidence and strategic know-how you need to succeed. Our resources at Interview Query are designed to provide you with a powerful toolkit, ensuring you are well-prepared to tackle your interview challenges head-on.

Dive into our company interview guides for additional preparation tips, and feel free to reach out with any questions. Good luck with your interview!