San Francisco, CA

Salary
$268,000–$336,000
Posted
Jul 16, 2026
Location
San Francisco, CA
Last confirmed open
Jul 21, 2026

What this job asks for AI summary

A senior research and engineering role focused on building autonomous systems that apply AI-driven scientific reasoning to life science discovery. The work centers on designing architectures and workflows that can represent biological hypotheses, handle uncertainty, integrate experimental evidence, and close the loop between computational predictions and laboratory automation. Best suited to someone with a strong ML research background who can also engage deeply with biological reasoning and experimental design.

Senior level · National · Doctorate required

Must have (2)
PyTorch, Jax or TensorFlowagentic systems
Nice to have (4)
causal inferenceactive learningsingle-cell omicsLLMs

“or” means any one of them counts — you don't need all of them.

We read this from the posting text with AI. Skim the description below before ruling yourself out.

How this req sits in the market our data

What the occupation pays Median $122,874 (middle half $87,544–$162,374).

Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.

Why we read it this way (6)

The PhD requirement is stated as a hard gate in the 'What You'll Need to Succeed' section with no 'or equivalent experience' escape clause, so it is treated as a minimum degree requirement.

PyTorch is listed as the primary example in a required 'hands-on experience in frameworks such as…' clause; JAX and TensorFlow are the named alternatives. The 'such as' qualifier means the specific tool is interchangeable, but hands-on experience with a modern ML framework is a hard gate.

Probabilistic modeling and agentic systems appear in the required section as part of the research track record and system-building requirements.

Bayesian modeling, causal inference, active learning, single-cell omics, and LLMs (foundation modeling) all appear under 'Bonus Points For' and are therefore preferred, not required.

The role sits at the intersection of ML research and software engineering; the alternative occupation 15-1252 (Software Developers) is a plausible runner-up given the emphasis on building production-quality research systems and agentic architectures.

Ignored 2 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): probabilistic modeling, Bayesian modeling.

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