ML Ops Cloud Engineer: Production ML Infra

WHOOP; Boston (MA); On-site; USD 125,000 - 175,000; Full time

Job summary

WHOOP is seeking a Software Engineer II to join the MLOps team in Boston, MA. You will design, build, and optimize cloud infrastructure to deploy and scale ML models, collaborating with Data Scientists and AI teams to move prototypes into production.

The role focuses on creating robust pipelines, APIs, and microservices for real-time ML inference, leveraging AWS SageMaker, Lambda, and ECS. A strong foundation in cloud concepts and software engineering is essential for success.

Qualifications

• 2+ years in software engineering focused on ML infrastructure in cloud environments.
• Familiar with cloud computing concepts and design patterns.
• Experience coding in Python or Java.
• Understanding of ML lifecycle from training to production monitoring.
• Excellent cross-functional collaboration.
• Curiosity to stay updated with ML/AI deployment trends.

Responsibilities

• Design, develop, and maintain cloud-based infrastructure for deploying and scaling ML models.
• Implement CI/CD pipelines for ML models across development to production.
• Collaborate with Data Scientists and AI teams to move prototypes to production.
• Develop APIs and microservices to enable real-time ML inference.
• Leverage AWS services (SageMaker, Lambda, ECS) for scalable ML deployment.
• Troubleshoot deployment and performance issues in production.
• Stay current with ML infrastructure and cloud deployment strategies.

Skills

Cloud computing concepts

Python or Java programming

ML lifecycle understanding

Collaboration skills

Curiosity for industry trends

Education

Bachelor’s degree in Computer Science, Software Engineering, or related field

Tools

AWS SageMaker

AWS Lambda

AWS ECS

Job description

WHOOP is seeking a Software Engineer II to join the MLOps team in Boston, MA. You will design, build, and optimize cloud infrastructure to deploy and scale ML models, collaborating with Data Scientists and AI teams to move prototypes into production.

The role focuses on creating robust pipelines, APIs, and microservices for real-time ML inference, leveraging AWS SageMaker, Lambda, and ECS. A strong foundation in cloud concepts and software engineering is essential for success.