Principal Machine Learning Engineer

Jobtailor; California (MO); On-site; USD 180,000 - 230,000; Full time

Job summary

Genentech is seeking a senior ML infrastructure and systems engineer to architect autonomous agents for drug discovery and to own production Python/PyTorch codebases. You will design memory architectures, interfaces to diverse data sources, and distributed training systems for foundation models, partnering with ML scientists and domain experts.

You will establish best practices for experiment tracking, observability, CI/CD, and infra, while shaping the long-term AI4DD roadmap and serving as a

Qualifications

• PhD with 5+ years or MS with 8+ years or BS with 10+ years of industry ML systems experience.
• Exceptional Python programming skills.
• Strong software eng fundamentals: Git, testing, CI/CD, docs, architecture.
• Extensive hands-on PyTorch and JAX experience.

Responsibilities

• Architect and deploy autonomous agents for drug discovery tasks.
• Design memory architectures and context management for long-horizon work.
• Build interfaces between agents and genomic, chemical, clinical data sources.
• Develop large-scale distributed training and inference systems for foundation models.
• Own production Python/PyTorch codebases transforming research into enterprise software.
• Establish best practices for experiment tracking, observability, CI/CD, and infra.
• Define long-term engineering roadmap for AI4DD agentic and foundation models.
• Serve as technical authority on ML infrastructure for Genentech leadership.
• Architect cross-functional platforms and elevate engineering standards across gRED.
• Collaborate with ML Scientists to translate scientific problems into deployable systems.

Skills

Python Programming

PyTorch

ML Infrastructure Deployment

Agent Orchestration Frameworks

Large-Scale ML Systems

Education

PhD with 5+ years

MS with 8+ years

BS with 10+ years

Tools

AWS

HPC Environments

LangGraph

MCP-based Tool Integration

Job description

• Architect and deploy autonomous agents that utilize tools, retrieve scientific evidence, and execute multi-step reasoning across drug discovery workflows
• Design and implement advanced agent memory architectures and context management for long-horizon scientific tasks
• Build reliable interfaces between agents and genomic, chemical, and clinical data sources
• Design, build, and optimize large-scale distributed training and inference systems for foundation models
• Own production Python/PyTorch codebases that turn research into enterprise-grade software
• Establish best practices for experiment tracking, system observability and monitoring, evaluation harnesses, CI/CD, and infrastructure
• Define the long-term engineering roadmap for AI4DD’s agentic and foundation models
• Serve as a technical authority on ML infrastructure for Genentech leadership
• Architect cross-functional platforms and elevate the engineering bar across gRED
• Partner with ML Scientists and domain experts to translate scientific problems into scoped, shippable, and efficient systems

Requirements

• BS, MS, or PhD in Computer Science, Machine Learning, Engineering, or a related quantitative field
• PhD with 5+ years, MS with 8+ years, or BS with 10+ years of industry experience building, shipping, and owning large-scale ML systems and infrastructure end-to-end
• Exceptional Python programming skills
• Rigorous software engineering fundamentals, including Git, automated testing, CI/CD, documentation, and architecture design
• Extensive hands-on experience with PyTorch and JAX
• Experience deploying ML infrastructure on AWS or HPC environments, including distributed training tools
• Practical experience designing agent orchestration frameworks, such as LangGraph or MCP-based tool integration
• Experience managing persistent agent memory and building self-improving loops
• Strong passion for applying frontier AI and agentic science to AI for Drug Discovery, biology, and chemistry
• Preferred: deep expertise in LLM serving, test-time compute, sampling/search strategies, model routing, batching, caching, and latency/cost/quality tradeoffs
• Preferred: experience with molecular modalities, including protein sequences, chemical graphs, and structured molecular data
• Preferred: public portfolio of significant technical contributions to open-source ML, systems, or MLOps libraries

Core Competencies

Demonstrates expertise in architecting and deploying autonomous agents for drug discovery, with a strong foundation in Python and PyTorch. Capable of designing large-scale ML systems and infrastructure while collaborating with cross-functional teams to translate scientific challenges into effective solutions.

Highest-signal resume keywords

• Python Programming
• PyTorch
• ML Infrastructure Deployment
• Agent Orchestration Frameworks
• Large-Scale ML Systems

Hard Skills

• Machine Learning
• Software Engineering Fundamentals
• Automated Testing
• CI/CD
• Architecture Design
• Agent Memory Management
• Distributed Training
• JAX
• Experiment Tracking
• System Observability

Soft Skills

• Collaboration
• Technical Authority
• Problem-Solving

Industry Keywords

• Drug Discovery
• AI for Drug Discovery
• Biology
• Chemistry
• Molecular Modalities

Tools & Technologies

• AWS
• HPC Environments
• LangGraph
• MCP-based Tool Integration