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Anthrogen

Member of Technical Staff (Research Engineer)

San FranciscoOnsite · United States
Staff · FullTime · Technical Staff
POSTED 7 JULVERIFIED 9H AGO
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Role
125 days opentrue age
Posted 7 Jul
Payats field
$150k–380kposted
▼ −4% vs market median $275k
$150k$380k
Market p25–p75
$248k–$300k
Anthrogen p25–p75
$265kn=4
SF Bay Area · n=174 · 365d to 6 Nov
CompanyAnthrogen →
8open roles
Opened · 30d
+0
Closed · 30d
−0
2d of snapshots · 30d window
Market
1,025open · Staff ML / AI
Opened · 7d
+10210% of open
All ml / ai
6,607open
All tracked boards · live 10 Nov · n=1,025
PythonPyTorch

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%.

Source · AnthrogenFirst seen 2 NovOriginal ↗