Job Description

Job Title: Staff ML Infrastructure Engineer

Location: Watertown, MA or New York, NY (Hybrid in-person ~2x per week)

Compensation: $184,000 $215,000 base salary + competitive equity

***Note: Visa sponsorship available (F-1 OPT with 1+ year remaining, H1B transfers). US based candidates only.

About the Role

We are seeking a Staff ML Infrastructure Engineer who thrives at the intersection of machine learning and cloud-native engineering. In this role, you will design, optimize, and scale the infrastructure that powers advanced ML platforms, ensuring models train faster, deploy more efficiently, and run reliably at scale. This is a mission-critical position that enables seamless collaboration between ML research and engineering, accelerating innovation in genetic medicine.

Key Responsibilities

• Own and optimize ML compute infrastructure: manage allocation, track usage/costs, and forecast future needs.

• Partner with engineers to evolve ML tooling and development environments, improving efficiency and reproducibility.

• Deploy ML models into production and improve inference performance.

• Manage vendor relationships (e.g., Google Cloud, Weights & Biases) with technical oversight and strategic planning.

• Support large-scale ML training and experimentation workflows.

• Balance innovation with execution in a fast-moving, mission-driven environment.

• Collaborate cross-functionally to deliver high-impact results.

Qualifications

• 48 years of experience as an ML Infrastructure Engineer, Data Engineer, or similar role (Staff-level).

• Experience building ML infrastructure and tools that scale across teams and evolve over time.

• Proven track record supporting large-scale ML training and experimentation

• Hands-on expertise in Python for scripting, automation, and infrastructure tooling.

• Strong experience with cloud-native environments (GCP or AWS), especially for ML workflows.

• Proficiency with containerized environments (Docker, Kubernetes)

• Experience with deep learning frameworks such as PyTorch or TensorFlow in production.

• BS in Computer Science, Machine Learning, Computational Biology, or related quantitative field.

Preferred Background

• Experience in both high-scale tech companies and Series AC startup environments.

• Prior involvement in cloud infrastructure transitions or system migrations.

• Exposure to AI/ML applications in biotech or computational biology.

• Experience managing compute budgets and vendor relationships.

Additional Details

• Hybrid work model: must be based in Boston or NYC with in-person presence ~2 days per week.

• Visa sponsorship available (F-1 OPT with 1+ year remaining, H1B transfers).