Machine Learning Engineer

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 #J-18808-Ljbffr