Member of Technical Staff (Research Scientist)
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- Work arrangement
- onsite
- Employment
- FullTime
- Stated compensation
- USD 150,000–380,000 1 YEAR
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Requirements
1 with evidence8 unclear or unstated- Remote policy
- onsite
“Provider workplace type: onsite”
From employer posting metadata - Employment type
- FullTime
- Compensation
- USD 150,000–380,000 1 YEAR
8 fields not stated in this posting
- Employer sponsorship
- Not specified
- Geographic scope
- Not specified
- Degree requirement
- Not specified
- Security clearance
- Not mentioned
- Timezone / hours
- Not specified
- Citizenship requirement
- Not mentioned
- Minimum experience (years)
- Not specified
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Original job description
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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 want researchers with great intuition in machine learning and solid fundamentals —irrespective of bio background. You'll drive original research on the architectures, training objectives, and scaling behind our models, owning an agenda from idea to result. We're a small, high-density team building frontier AI and the biological systems to validate it, and we index on research taste and output, not credentials.
What you'll do
Push on generative models (diffusion, autoregressive), architectures, optimization, and scaling.
Turn research directions into models that work, in close collaboration with our engineering team.
Help define what frontier AI for biology looks like.
Who you are
You have a track record of original research (first-author work at NeurIPS, ICML, ICLR, AISTATS, JMLR, TMLR, other top conferences/journals, impactful open models, or comparable research output).
Deep command of modern deep learning: generative models, architectures, optimization, and scaling.
You drive a research agenda independently, from idea to experiment to result.
High-agency, in person, and comfortable with the ambiguity of frontier research.
Bonus points
Range across subfields — a strong researcher who picks up new domains fast, or meaningful time in an ML-for-something domain (robotics, biology, physics).
Widely-used open-source models or research artifacts.
Familiarity with the protein-ML landscape.
Interest in biology, protein design, and AI for science.
Logistics
Full-time, onsite in San Francisco.
Equity: 0.05-0.5%.
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