Lila Sciences

3 months ago

Principal, Machine Learning Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 15 hours agoStatus: Live

Job Summary

Design, build, and scale large-scale ML infrastructure for life-science AI applications, spanning training pipelines, distributed GPU compute, deployment, and monitoring. Own end-to-end production ML systems and collaborate with AI scientists to translate research prototypes into robust, scalable production capabilities powering automated scientific discovery.

Location: San Francisco

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Sr. Manager level

Key Responsibilities

•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 lifecycle: build pipeline models, integrate experimental feedback loops, and ensure model outputs are actionable for downstream scientific workflows
•Define and advance ML engineering standards, tooling, and best practices across the AI organization

Pay and Benefits

Salary: USD 252,000 - 374,000 annually

Equity and Bonus:Equity

Perks:Health InsuranceDentalVision401kEquityRsusRemote WorkPaid Leave

Key Requirements

•Master's degree or higher in Computer Science, Machine Learning, or a related field (or Bachelor's with equivalent experience)
•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 (AWS, GCP, or on-prem)
•Strong software engineering fundamentals: system design, production-grade code, CI/CD, observability, and reliability practices
•Proficiency in ML frameworks (PyTorch, JAX, or TensorFlow) with experience optimizing training and inference performance

Experience:10+ yearsLife sciencesBiotechAI in life sciences

Education:Master's

Skills:CommunicationCollaborationProblem-solvingLeadershipInfluencing

Languages:English

Tech Stack:PythonPyTorchJAXTensorFlowCI/CDAWSGCPGPUDistributed trainingOn-prem

Company Brief

Lila Sciences

Develops AI-driven platforms to accelerate drug discovery and biological research by integrating machine learning with chemical and biological data to predict molecular properties, streamline candidate selection, and enable faster therapeutic development.

Industry: Biotech

Company Size: Small (11 to 50 employees)

Growth: Early Stage Startup

Funding: Seed

Website LinkedIn

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Lila Sciences

3 months ago

Principal, Machine Learning Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 15 hours agoStatus: Live

Job Summary

Location: San Francisco

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Sr. Manager level

Key Responsibilities

•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 lifecycle: build pipeline models, integrate experimental feedback loops, and ensure model outputs are actionable for downstream scientific workflows
•Define and advance ML engineering standards, tooling, and best practices across the AI organization

Pay and Benefits

Salary: USD 252,000 - 374,000 annually

Equity and Bonus:Equity

Perks:Health InsuranceDentalVision401kEquityRsusRemote WorkPaid Leave

Key Requirements

•Master's degree or higher in Computer Science, Machine Learning, or a related field (or Bachelor's with equivalent experience)
•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 (AWS, GCP, or on-prem)
•Strong software engineering fundamentals: system design, production-grade code, CI/CD, observability, and reliability practices
•Proficiency in ML frameworks (PyTorch, JAX, or TensorFlow) with experience optimizing training and inference performance

Experience:10+ yearsLife sciencesBiotechAI in life sciences

Education:Master's

Skills:CommunicationCollaborationProblem-solvingLeadershipInfluencing

Languages:English

Tech Stack:PythonPyTorchJAXTensorFlowCI/CDAWSGCPGPUDistributed trainingOn-prem

Company Brief

Lila Sciences

Industry: Biotech

Company Size: Small (11 to 50 employees)

Growth: Early Stage Startup

Funding: Seed

Website LinkedIn