Sr. Machine Learning Engineer - Machine Learning

Role overview

This senior machine learning engineering role focuses on building the models, infrastructure, and AI-enabled tools used in scientific research and protein design. The work combines research collaboration with rigorous software engineering, spanning experimentation, distributed training, GPU optimization, inference systems, and production-quality internal platforms. The position is full time and may be based remotely or in Watertown, Massachusetts.

Responsibilities

- Create modular, portable training systems that support the development and iteration of protein design models. - Improve the scalability, reliability, and performance of machine learning training and inference workflows. - Investigate bottlenecks through profiling, resource analysis, custom kernels, and accelerated computing techniques. - Develop repeatable agentic AI workflows that increase research productivity while preserving safety and reliability. - Work with research scientists, protein engineers, and other machine learning engineers to turn prototypes into reusable systems. - Contribute across the stack, including model development, GPU-level optimization, distributed execution, orchestration, documentation, and technical planning.

Requirements

- At least five years of professional experience developing software for machine learning. - Strong software engineering fundamentals, including object-oriented design, testing, version control, dependency management, and API design. - Hands-on experience containerizing applications for remote environments with Docker and Kubernetes. - Experience with large-scale distributed training or inference using Ray or a comparable framework. - Familiarity with machine learning performance engineering, including profiling, resource analysis, bottleneck diagnosis, and custom kernel development. - Experience owning complex systems from requirements and design through implementation, rollout, and ongoing maintenance.

Nice to have

- Professional or academic experience in machine learning research or scientific computing. - Experience building internal platforms or developer tools, along with familiarity with MLOps practices such as monitoring, versioning, CI/CD, and model registries. - GPU programming experience with technologies such as CUDA or Triton. - Experience using modern agentic AI tools for software development. - Exposure to biology, bioinformatics, structural biology, or protein modeling.

Benefits and work setup

The role offers a high-trust, mission-driven research environment, with compensation that may include performance-based bonuses and stock options. Listed benefits include medical, dental, and vision coverage, a 401(k) plan, flexible paid time off and holidays, a company-provided laptop, commuter support, onsite meals, and access to an onsite gym.