Software Engineer II, ML Ops

Generate Biomedicines•Somerville, MA

16h

About The Position

Generate:Biomedicines is a new kind of therapeutics company – existing at the intersection of machine learning, biological engineering, and medicine – pioneering Generative Biology™ to create breakthrough medicines where novel therapeutics are computationally generated, instead of being discovered. The Company has built a machine learning-powered biomedicines platform with the potential to generate new drugs across a wide range of biologic modalities. This platform represents a potentially fundamental shift in what is possible in the field of biotherapeutic development. We pursue this audacious vision because we believe in the unique and revolutionary power of generative biology to radically transform the lives of billions, with an outsized opportunity for patients in need. We are seeking collaborative, relentless problem solvers that share our passion for impact to join us! Generate:Biomedicines was founded in 2018 by Flagship Pioneering and has received nearly $700 million in funding, providing the resources to rapidly scale the organization. The Company has offices in Somerville and Andover, Massachusetts with 300+ employees. The Role: We are seeking a Software Engineer II to help build and evolve the platform that powers machine learning at Generate. You 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.

Requirements

2+ years of experience in software or ML engineering, ideally building applications, infrastructure, or data systems for ML workloads.
Strong proficiency in Python and a solid understanding of software design principles and patterns.
Experience using AI-assisted development tools (e.g., code copilots, LLM-based workflows) to improve engineering productivity.
Familiarity working with data pipelines, ETL processes, and relational databases such as PostgreSQL or Redshift.
Experience with distributed systems and containerized deployments on AWS using ECS or Kubernetes.
Familiarity with CI/CD, observability, and infrastructure as code tools.

Responsibilities

Build and extend core services for model training, evaluation, and large-scale inference across our ML platform.
Develop and maintain data pipelines and infrastructure to support large-scale datasets, including ETL workflows and data warehousing systems.
Contribute to optimizing and scaling workloads across GPU clusters, improving resource utilization, performance, and cost efficiency for training and inference jobs.
Collaborate with scientists, engineers, and product managers to define requirements and drive system improvements.