Principal, Machine Learning Engineer

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

Offered From the posting source checked Jun 20, 2026

Verification

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Salary

$252K-$374K From the posting source checked Jun 20, 2026

401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

169% above the BLS role benchmark for software engineering aggregate.

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

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

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Principal From the posting source checked Jun 20, 2026

Schedule

Shift type

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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

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Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Principal, Machine Learning Engineer 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 Science AI (LSAI), ML engineers build and operate the systems that turn generative models and reasoning frameworks into production capabilities powering automated scientific discovery across Lila's life science domains. We are seeking a Principal ML Engineer to design, build, and scale the ML infrastructure behind models spanning biological sequence design, molecular structure prediction, antibody engineering, and multimodal scientific reasoning. You will own critical systems end to end, from training pipelines and distributed compute to model deployment and integration into Lila's closed-loop discovery engine. This is a high-impact IC role for someone who operates at the intersection of ML systems engineering and life science applications. You will shape the technical direction for how ML models are trained, evaluated, and deployed at scale, collaborate closely with AI scientists and experimental researchers to close the computational-experimental loop, and drive Lila's ML infrastructure toward the next generation of capabilities. What You'll Be Building - Design, build, and optimize large-scale training pipelines for generative models on biological and chemical data, including distributed training across GPU clusters - Own production ML systems end to end: model deployment, serving infrastructure, monitoring, and reliability for models used in Lila's scientific workflows - Architect ML infrastructure that supports rapid iteration across sequence design, structure prediction, and multimodal scientific reasoning workloads - Drive the engineering side of Lila's "Lab-in-the-Loop"

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