Principal / Sr. Principal BioML Scientist

Benefits

Parental leave

Not verified

Non-birth-parent leave

Not verified

Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

Not verified

Relocation assistance

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Childcare support

Not verified

Learning budget

Offered From the posting source checked Jun 20, 2026

Verification

Not verified

Salary

$288K-$480K From the posting source checked Jun 20, 2026

401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$55,390 U.S. median for this role

Projected growth (BLS Employment Projections)

-0.1% - Decline

593% above the BLS role benchmark for teaching and education aggregate.

Posted salary is far from this role benchmark; treat it as low confidence.

Matched to SOC 25-2021 - Teaching and Education aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Teaching Education From the posting source checked Jun 20, 2026

Seniority

Principal From the posting source checked Jun 20, 2026

Schedule

Shift type

Not verified

Weekend work

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Company

Equity

Offered From the posting source checked Jun 20, 2026

Application

Cover letter

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Assessment

Not verified

Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Principal / Sr. Principal BioML Scientist San Francisco, CA USA Your Impact at LILA Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Sciences AI (LSAI), we are standing up a new AI for Cell Biology team to develop autonomous-science capabilities for cellular and tissue biology, spanning single-cell omics, perturbation biology, spatial profiling, imaging, genetics, and multi-modal experimental data. We are seeking a Principal or Sr. Principal BioML Scientist to be a co-architect of how Lila's autonomous-science platform changes cell biology and to own the applied and translational BioML charter that turns those platform capabilities into real-world therapeutic impact. The team's ML lead owns core model strategy and inference architecture; the Engineering lead owns platform infrastructure; this role adds the applied scientific perspective into platform shape: deciding what closed loops are worth running, what kinds of scientific questions become tractable when AI and lab automation co-evolve, and what evidence standard turns a model output into an experimental decision. The platform isn't something this role consumes ; it's something this role helps build , from the applied science side. This role grows and leads the team's applied science footprint : a group of domain-embedded scientists working across disease areas and therapeutic modalities (cell therapy, nucleic-acid delivery, small molecule). The initial applied focus is target identification as the entry point into cell-biology-grounded therapeutic discovery, with the scope broadening over time as the team and the platform mature. This is a senior individual

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