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.

• 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 Sage Maker, 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.

• 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.

WHOOP is an Equal Opportunity Employer and participates in E-verify to determine employment eligibility. The WHOOP compensation philosophy is designed to attract, motivate, and retain exceptional talent by offering competitive base salaries, meaningful equity, and consistent pay practices that reflect our mission and core values.

At WHOOP, we view total compensation as the combination of base salary, equity, and benefits, with equity serving as a key differentiator that aligns our employees with the long-term success of the company and allows every member of our corporate team to own part of WHOOP and share in the company’s long-term growth and success.

The U.S. base salary range for this full-time position is $125,000 - $175,000. Salary ranges are determined by role, level, and location. Within each range, individual pay is based on factors such as job-related skills, experience, performance, and relevant education or training. In addition to the base salary, the successful candidate will also receive benefits and a generous equity package. These ranges may be modified in the future to reflect evolving market conditions and organizational needs.

While most offers will typically fall toward the starting point of the range, total compensation will depend on the candidate’s specific qualifications, expertise, and alignment with the role’s requirements.