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Software Engineer II (MLOps)

At WHOOP, we're on a mission to unlock human performance. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives.


We are looking for a talented and passionate Software Engineer II to join our MLOps team, focusing on the development and optimization of ML cloud infrastructure. In this role, you will play a critical part in supporting our Data Science and AI teams by building robust, scalable systems for the productionalization of machine learning models. Your work will be at the heart of bringing advanced ML/AI solutions into production, ensuring they are reliable, scalable, and ready to drive value across WHOOP.


RESPONSIBILITIES:
  • Design, develop, and maintain cloud-based infrastructure to support the deployment and scaling of machine learning models. Implement automated pipelines for continuous integration and continuous deployment (CI/CD) of ML models, ensuring seamless transitions from development to production environments.
  • Collaborate closely with Data Scientists and AI teams to understand model requirements and facilitate the transition from prototype to production. 
  • Develop APIs, microservices, and other components necessary to integrate ML models into existing systems, enabling real-time inference and decision-making.
  • Leverage cloud services to optimize the deployment and performance of machine learning models and associated infrastructure. Utilize services such as AWS SageMaker, Lambda, and ECS to build scalable, cost-effective solutions that support real-time ML/AI workloads.
  • Support AI teams by troubleshooting and resolving technical challenges related to model deployment and performance in production.
  • Stay up-to-date with the latest advancements in ML infrastructure, cloud computing, and AI deployment strategies. Proactively suggest and implement improvements to enhance the efficiency, reliability, and scalability of ML operations within the organization.


QUALIFICATIONS:
  • Bachelor’s Degree: A degree in Computer Science, Software Engineering, or a related field; or equivalent practical experience.
  • 2+ years of experience in software engineering, with a significant focus on building and maintaining ML infrastructure in cloud environments.
  • Familiarity with Cloud Computing concepts and design patterns.
  • Experience in writing code in a functional or object oriented programming language such as Python or Java.
  • An understanding of the steps of ML lifecycle including model training and evaluation as well as monitoring a model in production.
  • Excellent collaboration skills, with the ability to work closely with Data Scientists, AI and Software teams, and other cross-functional stakeholders.
  • A high level of curiosity to keep up with industry trends and a proven ability to find creative solutions to novel problems.


This role is based in the WHOOP office located in Boston, MA. The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office. 


Interested in the role, but don’t meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply.


WHOOP is an Equal Opportunity Employer and participates in E-verify to determine employment eligibility.  It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

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What You Should Know About Software Engineer II (MLOps), WHOOP

At WHOOP, we're on a mission to unlock human performance, and we need a talented Software Engineer II to join our dynamic MLOps team in Boston, MA. If you're passionate about machine learning and have a knack for building robust cloud infrastructure, this is the role for you! As a Software Engineer II at WHOOP, you'll dive into the world of machine learning, playing a vital role in developing and optimizing infrastructure that supports our Data Science and AI teams. You’ll work on designing scalable systems that facilitate the productionalization of ML models, ensuring they’re reliable and ready to deliver exceptional value across our organization. Your day-to-day will involve collaborating with brilliant Data Scientists and AI professionals to understand model requirements and make sure everything runs seamlessly from development to production. And let's not forget, you’ll have the opportunity to implement modern CI/CD practices and develop APIs and microservices to integrate ML models into our systems. Staying at the forefront of ML operations means you’ll be actively seeking out the latest trends in cloud computing to enhance efficiency and reliability. So if you're ready to take your career to the next level and make a real impact at WHOOP, come join us in Boston and unlock your full potential!

Frequently Asked Questions (FAQs) for Software Engineer II (MLOps) Role at WHOOP
What responsibilities does the Software Engineer II role at WHOOP entail?

The Software Engineer II position at WHOOP involves designing, developing, and maintaining cloud-based infrastructure to support machine learning model deployment and scaling. This includes implementing automated CI/CD pipelines, collaborating with Data Scientists to transition models from prototype to production, developing APIs and microservices, and optimizing the performance of ML models on cloud services.

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What qualifications are needed to apply for the Software Engineer II position at WHOOP?

To be eligible for the Software Engineer II role at WHOOP, candidates should have a Bachelor’s Degree in Computer Science, Software Engineering, or a related field, along with at least 2 years of experience in software engineering with a focus on ML infrastructure in cloud environments. Familiarity with cloud computing concepts, programming languages such as Python or Java, and a good understanding of the ML lifecycle are also essential.

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What is the work environment like for the Software Engineer II at WHOOP?

Working as a Software Engineer II at WHOOP means joining a collaborative and innovative environment. You will closely interact with Data Scientists and AI teams, contributing to the seamless integration of advanced ML models. The WHOOP office in Boston, MA, fosters a culture of curiosity and learning, supporting employees to stay updated with industry trends.

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Is experience with specific cloud services required for the Software Engineer II role at WHOOP?

Yes, candidates for the Software Engineer II position at WHOOP should have experience leveraging cloud services, especially AWS tools like SageMaker, Lambda, and ECS. This technical expertise is crucial for building scalable, cost-effective solutions that support real-time ML workloads.

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What does WHOOP value in candidates applying for the Software Engineer II position?

At WHOOP, we value character as much as experience. We recognize that every candidate has unique strengths, so even if you don't meet every qualification outlined for the Software Engineer II role, we encourage you to apply. We're committed to building a diverse and inclusive environment.

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Common Interview Questions for Software Engineer II (MLOps)
Can you describe your experience with cloud-based infrastructure?

When answering this question, be sure to highlight specific cloud services you've worked with, such as AWS or Azure. Discuss your role in building or maintaining ML infrastructure, emphasizing how you utilized these technologies for scalability and efficiency.

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How do you ensure the successful deployment of machine learning models?

Discuss your understanding of the ML lifecycle and the importance of CI/CD processes. Provide examples of how you've implemented automated pipelines and worked collaboratively with other teams to ensure smooth transitions from development to production.

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What programming languages are you proficient in, and how have you used them in previous projects?

Mention the programming languages you are most comfortable with, such as Python or Java. Provide examples of how you've applied your coding skills to solve specific problems or build features related to machine learning infrastructure.

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How do you approach troubleshooting issues in ML model deployment?

Emphasize a systematic approach to troubleshooting that includes identifying potential bottlenecks, analyzing logs, and collaborating with cross-functional teams for swift resolution. Mention any specific tools or methodologies you've used, showcasing your technical problem-solving skills.

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What strategies do you use to stay current with advancements in ML and cloud technologies?

Talk about your commitment to continuous learning, including attending workshops, participating in online courses, or engaging with industry forums. Highlight any specific resources or communities you follow to stay informed about the latest trends and best practices.

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Describe a challenging problem you've solved related to MLOps.

Share a specific example of a challenge you faced in MLOps, detailing the steps you took to address it. Focus on the problem-solving process, key insights gained, and the successful outcome achieved, demonstrating your critical thinking and technical skills.

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What do you believe is the most critical factor for the success of machine learning in production?

Provide insights into the importance of reliability, scalability, and monitoring as critical components. You can discuss your experiences with performance metrics or feedback loops that inform improvements in deployed models.

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How would you handle a disagreement with a Data Scientist about a model's implementation?

Highlight the importance of communication and collaboration. Discuss how you’d engage in respectful dialogue, present data-driven arguments, and ultimately prioritize achieving the project’s goals while maintaining a positive working relationship.

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What role do microservices play in your approach to MLOps?

Explain how microservices architecture can facilitate modular and scalable deployments of ML models. Provide examples of how you’ve utilized microservices to integrate ML solutions into larger systems, underscoring the advantages they offer in terms of flexibility and maintenance.

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Why do you want to work at WHOOP as a Software Engineer II?

Reflect on WHOOP's mission of unlocking human performance and how it resonates with you. Discuss what excites you about the opportunity to work on innovative ML solutions and the potential impact of your contributions on users’ lives.

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Our mission at WHOOP is to unlock human performance. We believe that every individual has an inner potential that can be enhanced through continuous monitoring. As such we've built a system across hardware, software, and analytics designed to coll...

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March 20, 2025

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