We Love AI JobsMember of Technical Staff (Research Engineer)
AnthrogenYou’ll build the systems that support Anthrogen’s AI models, including distributed training, GPU optimization, data pipelines, and inference serving. The role partners closely with researchers and owns infrastructure problems from initial design through production.
Skills & Expertise
Key Responsibilities
Build and scale distributed training, GPU optimization, and high-throughput data pipeline systems.
Develop inference and serving systems capable of running at scale.
Partner with researchers to turn ideas into robust infrastructure and production systems.
Full Description
About Anthrogen
Anthrogen is engineering post-modality biology. Today's modalities reflect historical contingencies in biological progress, not fundamental categories. We develop the AI systems that design modular biological machines — and the experimental infrastructure to instantiate them. We're a small, high-density team in San Francisco building frontier AI and the biological systems to validate it, and we care most about people who move fast, go deep, and pick up whatever the problem needs.
The role
We're looking for exceptional engineers excited about frontier AI, especially for the sciences — irrespective of bio background. You'll build and scale the systems behind our models: distributed training, data infrastructure, and high-throughput inference, owning hard problems end to end. Biology is the domain, but this is a frontier systems job first.
What you'll do
· Build and scale distributed training, GPU optimization, and high-throughput data pipelines.
· Own inference and serving systems that run at scale.
· Work shoulder-to-shoulder with researchers, turning ideas into robust infrastructure.
· Take hard, ambiguous problems from zero to production and own them end to end.
Who you are
· You can write exceptional, production-grade code (strong Python plus a systems language), demonstrable through shipped systems, open source, or competition results.
· Hands-on with large-scale ML systems: distributed training, GPU optimization, high-throughput data pipelines, or inference and serving at scale.
· High-agency, in person, and willing to do whatever the problem needs.
Bonus points
· A track record of shipping research infrastructure or working closely with researchers.
· An AI-native workflow — you get real leverage from AI coding tools.
· Competitive programming or olympiad background (IOI, ICPC, Putnam, and the like).
· Meaningful open-source contributions to ML infrastructure (PyTorch, JAX, CUDA).
· Genuine interest in biology and AI for science.
Logistics
Full-time, onsite in San Francisco.
Equity: 0.05-0.5%.