Senior Machine Learning Engineer, Agentic Science/Generative Models, AI for Biology & Translation (AIBT)

The PositionA healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Roche.Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness the transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide. ​The OpportunityThe AI Biology & Translation (AIBT) department within Genentech's Computational Sciences Center of Excellence (CS-CoE) is building the next generation of AI systems for biology. Our mission is to develop AI models that learn from biological data at unprecedented scale, generating new insights into disease mechanisms, therapeutic opportunities, and human biology. We seek a highly motivated and passionate Senior ML Engineer to join our Generative Modeling team and help build and scale foundation models and agentic systems for therapeutic discovery. The successful candidate will contribute to the design, development, and scaling of large-scale foundation models and AI agents, with the ultimate aim of accelerating target and drug discovery. This role spans the full stack: the agent design and orchestration logic that makes these systems scientifically useful, and the infrastructure, AgentOps, and MLOps that make them robust, reproducible, and efficient at scale. Depending on team coverage at a given time, you may own infrastructure end-to-end or partner with platform engineering on it, this role needs someone comfortable doing either. You'll join a multidisciplinary environment alongside ML scientists, ML engineers, and computational biologists. The ideal candidate combines strong software and ML engineering skills, a systems mindset, fluency in how agentic systems are actually built and evaluated, and a "get-it-done" attitude.In this role, you will:Agentic systemsBuild agents that use tools, retrieve evidence, and reason across multi-step scientific workflowsBuild reliable interfaces between agents and biological, genomic, and clinical data sourcesDesign evaluation harnesses that check agent output against scientific ground truthDesign and implement self-improving and autonomous loops for autoML and lab in the loopImplement agent memory and context management for long-horizon workflowsModels and production systemsBuild, finetune, deploy, and scale foundation models and LLMs in productionOwn production Python/PyTorch (or JAX) codebases that turn fast-moving research ideas into reliable, reusable softwareOwn the MLOps/AgentOps lifecycle: experiment tracking, evaluation, monitoring, reproducibility, CI/CD, and infrastructure-as-code (Terraform, Helm, Kubernetes)CollaborationWork with research scientists to turn open-ended scientific problems into scoped, shippable systemsRaise the engineering bar across gRED and RocheWho you areBS/MS in CS, ML, engineering, or a related quantitative field5+ years building and shipping ML systems in industryExcellent Python; strong software and data engineering fundamentals (Git, automated testing, CI/CD, documentation)Track record leading technical projects end to endComfort with ambiguity and close collaboration with scientistsStrong problem-solving and communication skillsInterest or experience in applying ML to scientific discovery (AI for science), such as biology, chemistry, or drug discovery, including working with domain-specific data and models.PreferredInference-time scaling and optimization: test-time compute, sampling and search strategies, model routing, batching, caching, latency/cost/quality tradeoffsML infrastructure on AWS (EC2, S3, EKS, SageMaker), including distributed training and inference on HPCEvaluation systems for agentic applications where correctness is scientifically definedAgent orchestration frameworks in production (LangGraph, MCP-based tool integration)Relocation benefits are NOT available for this job postingThe expected salary range for this position based on the primary location of San Francisco is $168,100 - 312,300 of hiring range. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below.Benefits#ComputationCoE#tech4lifeComputationalScienceGenentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.Job SummaryJob number: 202608-120617Date posted : 2026-08-10Profession: Data Science & AI / MLEmployment type: Full time