Principal ML Systems Engineer — Life Sciences

Lila Sciences; San Francisco (CA); On-site; USD 252,000 - 374,000; Full time

Benefits offered by this job

Competitive base compensation

Generous early-stage equity

Comprehensive benefits program

Flexible time off

Paid parental leave

Job summary

Lila Sciences in San Francisco is looking for a Principal ML Engineer to design and scale the machine learning infrastructure for scientific applications. This role involves developing large-scale training pipelines, managing end-to-end ML systems, and collaborating across teams to bridge the gap between machine learning research and production. Candidates should have a Master's in Computer Science or a related field, 10+ years in production ML, and deep expertise in distributed training systems. A competitive compensation package is offered.

Qualifications

• 10+ years of hands-on experience building and operating production ML systems at scale.
• Deep expertise in distributed training infrastructure with large-scale GPU clusters.
• Strong fundamentals in system design, production-grade code, and CI/CD.

Responsibilities

• Design, build, and optimize large-scale training pipelines for generative models.
• Own production ML systems from deployment to monitoring.
• Collaborate with AI scientists to bridge research and deployment.

Skills

Production ML systems

Distributed training infrastructure

ML frameworks (PyTorch, JAX, TensorFlow)

Software engineering fundamentals

Cross-functional collaboration

Education

Master's degree in Computer Science or related field

Job description

Lila Sciences in San Francisco is looking for a Principal ML Engineer to design and scale the machine learning infrastructure for scientific applications. This role involves developing large-scale training pipelines, managing end-to-end ML systems, and collaborating across teams to bridge the gap between machine learning research and production. Candidates should have a Master's in Computer Science or a related field, 10+ years in production ML, and deep expertise in distributed training systems. A competitive compensation package is offered.