Software Engineer II, MLOps

Whoop•Boston, MA

9d•$125,000 - $175,000•Onsite

About The Position

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.

Requirements

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.

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.