View role → : ML Engineer, San Francisco

Proteus Bio; San Francisco (CA); Hybrid; USD 150,000 - 210,000; Full time

Job summary

Proteus Bio in San Francisco is seeking an ML Engineer to join our Engineering team on-site at Fort Mason. You will help build our Biological Intelligence Platform for autonomous discovery of personalised medicine, taking ideas from concept to production.

You should have strong ML engineering fundamentals, experience with post-training methods (SFT, RL, preference optimization, evals, reward models, data pipelines), and a bias toward ownership and rapid learning in biology.

Qualifications

• Strong ML engineering fundamentals and ability to take systems from idea to production.
• Experience with post-training — SFT, RL, preference optimization, evals, reward models, or data pipelines.
• Operates with a high degree of autonomy and ownership; fast learner.
• Biology knowledge is valuable but not required; willingness to learn is key.

Skills

ML engineering

Production systems

Autonomy

Biology curiosity

Tools

PyTorch

Hugging Face Transformers

TRL

Accelerate

DeepSpeed

FSDP

RLHF

Job description

We are hiring an ML Engineer to join Proteus in San Francisco.

We are building a Biological Intelligence Platform for the autonomous discovery of personalised medicine. Our platform reasons over disease targets, designs therapeutic and diagnostic candidates, and continuously learns from experimental results.

Location: San Francisco. Employment type: Full time.

Workplace: On-site.

Start: September 2026. Department: Engineering.

We are looking for someone who

• Has strong ML engineering fundamentals and can take systems from idea to production
• Has experience with post-training — SFT, RL, preference optimization, evals, reward models, or data pipelines
• Operates with a high degree of autonomy
• Ideally knows biology, although curiosity and the ability to learn quickly matter more
• Wants to leave the world better than they found it

Preferred Experience

• PyTorch and Hugging Face: Transformers, TRL, Accelerate
• SFT / instruction tuning
• LoRA, QLoRA and PEFT
• DPO / g-DPO for preference training
• GRPO / PPO for online reinforcement learning
• Reward models, rule-based verifiers and outcome-based rewards
• Tool-use trajectory collection and credit assignment
• Distributed GPU training: DeepSpeed, FSDP, mixed precision
• Model evaluation, checkpointing and experiment tracking

Biology experience is valuable, but not required. We care more about what you have built, how you think, and whether you take ownership than where you went to school.

We are backed by Founders, Inc. and work in person from their Fort Mason campus.