Senior Machine Learning Engineer

Harnham; San Francisco (CA); Hybrid; USD 220,000 - 240,000; Full time

Job summary

Join a mission-driven biotech startup as a Machine Learning Engineer in San Francisco. You'll lead the development and maintenance of ML infrastructure powering cancer therapy solutions, collaborating with experts in a role focused on impactful innovation. With a competitive salary and flexible work arrangements, this position is ideal for those looking to make a significant contribution in an exciting field.

Benefits

Flexible work environment

Generous health, dental and vision benefits

Meaningful equity

Qualifications

• 2+ years of industry experience in ML infrastructure.
• Proven experience managing large-scale GPU clusters in the cloud.
• Hands-on experience with Kubernetes and PyTorch.

Responsibilities

• Build and maintain cloud-based GPU clusters primarily on AWS.
• Develop systems for distributed training and scalable inference of ML models.
• Ensure observability and stability of ML infrastructure.

Skills

AWS

GPU clusters

Container orchestration

PyTorch

Distributed ML frameworks

Job description

This range is provided by Harnham. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

$220,000.00/yr - $240,000.00/yr

Direct message the job poster from Harnham

Location: San Francisco, CA (Hybrid or Remote Flexibility Available)

Company Overview

Working with a mission-driven biotech startup that is leveraging advanced ML/AI technologies to develop cancer therapies.

Why Join Us?

• Help shape a high-growth startup in a high-impact therapeutic space.
• Be a key contributor to an early-stage company working on real-world cancer solutions.
• Work alongside a technically excellent and mission-aligned team.
• Join a culture that values collaboration, humility, and clear communication.

Role Summary

As a Machine Learning Engineer, you will lead the development and maintenance of the ML infrastructure powering large-scale model training and inference workflows. You'll work on scalable, distributed systems and seamless model experimentation. You'll also contribute directly to the ML codebase and help establish best practices for observability and performance.

Key Responsibilities

• Build, maintain, and operate cloud-based GPU clusters (primarily AWS).
• Develop and manage systems for distributed training and scalable inference of ML models.
• Collaborate with ML researchers and software engineers to design a flexible and robust ML Platform.
• Create tools and systems to enable fast analysis and inspection of large-scale model outputs.
• Debug, optimize, and contribute to core ML pipelines and codebases.
• Ensure observability, reproducibility, and stability of ML infrastructure and workflows.

Qualifications

Required Experience

• 2+ years of industry experience in ML infrastructure or similar roles.
• Proven track record managing large-scale GPU clusters in the cloud.
• Strong experience with AWS (SageMaker experience a plus).
• Hands-on experience with container orchestration (Kubernetes).
• Experience with PyTorch and deploying models at inference scale.

Preferred

• Experience with distributed ML frameworks (e.g., Ray).
• Familiarity with data loader optimization and performance tuning.
• Exposure to biotech, life sciences, or healthcare domains.

Compensation

• Expected base salary in the 220-240k range + meaningful equity
• Flexible work environment (remote/hybrid options).
• Generous benefits, including health, dental, and vision.

Apply now to help build the future of cancer therapeutics with cutting-edge ML.

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Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Science and Engineering

• Industries

  Biotechnology Research and Pharmaceutical Manufacturing

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