Senior Software Engineer, ML Ops

The Role

We are seeking an experienced and pragmatic Senior Software Engineer to build and evolve the platform that powers machine learning  will develop reliable, scalable, and observable systems that enable efficient model training, evaluation, and deployment for protein design. You will work closely with ML researchers, data scientists, and infrastructure engineers to translate research workflows into production‑grade systems. This role requires 3 or more days/week onsite in our Somerville, MA location.

• Build, maintain, and scale distributed ETL pipelines to integrate proprietary and public datasets into ML model training
• Design and deliver core services for model training, evaluation, and deployment across the ML platform.
• Collaborate with scientists, engineers, and product managers to define requirements and drive system improvements.
• Ensure reproducibility, observability, and cost efficiency across ML workflows.
• Contribute to the next generation of AI driven workflow orchestration, enabling researchers to run and evaluate models autonomously.
• Implement and evolve APIs and microservices with the cloud engineering team using AWS services and infrastructure as code to automate and standardize workflows.

5 or more years of experience in software engineering, ideally building infrastructure or data systems for ML workloads.

• Practical experience designing, monitoring, and optimizing data pipelines and relational databases such as Postgre
  
  SQL or Redshift.
• Strong proficiency in Python and solid understanding of software design principles and patterns.
• Experience with distributed systems and containerized deployments on AWS using Docker, ECS, or Kubernetes.
• Familiarity with CI/CD, observability, and infrastructure as code tools.