Principal Machine Learning Engineer, Foundation Models, AI for Drug Discovery

F. Hoffmann-La Roche AG; New York (NY); On-site; USD 193,000 - 358,000; Full time

Job summary

Genentech, a Roche subsidiary, seeks a Principal Machine Learning Engineer to join our Foundation Models team in AI for Drug Discovery. You will own the MLOps/AgentOps stack, architect autonomous agents that leverage tools, and drive multi-step reasoning across drug-discovery workflows.

We expect leadership in building large-scale ML systems, production Python/PyTorch codebases, and a roadmap for AI4DD platforms. Onsite presence is expected; relocation is not offered.

Qualifications

• PhD with 5+ years, MS with 8+ years, or BS with 10+ years of building and owning large-scale ML systems.
• Strong Python and software engineering fundamentals.
• Experience with distributed training and ML infra on AWS or HPC.

Responsibilities

• Own the MLOps/AgentOps stack for Foundation Models and establish evaluation harnesses.
• Architect and deploy autonomous agents that use tools and retrieve evidence across data sources.
• Design scalable training and inference systems and maintain production Python/PyTorch codebases.
• Define the long-term engineering roadmap and serve as ML infra lead.
• Translate research problems into efficient, shippable systems.

Skills

Python

Git

CI/CD

PyTorch

JAX

AWS

HPC

Agent orchestration

LangGraph

MCP-based tooling

Education

BS in Computer Science or related

MS in Computer Science or related

PhD in Computer Science or related

Tools

Git

PyTorch

JAX

AWS

Job description

A 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 organizations 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 maximizing 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 Opportunity:

At Roche's AI for Drug Discovery (AIDD) group (Prescient Design), we are revolutionizing drug discovery with cutting-edge machine learning. We are seeking a Principal Machine Learning Engineer to join our Foundation Models team.

• Own the MLOps/AgentOps Stack for Foundation Models: Establish rigorous evaluation harnesses that check agent output against scientific ground truth, and the experiment tracking, system observability and monitoring, CI/CD, and infrastructure those systems depend on.
• Agentic Systems Engineering: 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.
• Scalable ML Systems & Productionization: Design, build, and optimize large-scale distributed training and inference systems for foundation models. Own the production Python/PyTorch codebases that turn fast-moving research into enterprise-grade software.
• Technical Strategy & Leadership: Define the long-term engineering roadmap for AI4DD’s agentic and foundation models. Serve as a technical authority on ML infra for Genentech leadership, architect cross-functional platforms, and elevate the engineering bar across gRED.
• Research-to-Production Translation: Partner closely with ML Scientists and domain experts to translate open-ended scientific problems and complex reasoning objectives into scoped, shippable, and highly efficient systems.

Who You Are:

Education & Experience:

BS, MS, or PhD in Computer Science, Machine Learning, Engineering, or a related quantitative field. You have a demonstrated track record of technical leadership with increasing levels of experience based on degree: 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.

Engineering Foundation:

Exceptional Python programming skills and rigorous software engineering fundamentals (Git, automated testing, CI/CD, documentation, architecture design).

Deep Learning & Distributed Systems:

Extensive hands-on experience with modern deep learning frameworks (PyTorch, JAX) and deploying ML infrastructure on AWS or HPC environments, including distributed training tools.

Agentic Systems:

Practical experience designing agent orchestration frameworks (e.g., LangGraph, MCP-based tool integration), managing persistent agent memory, and building self-improving loops.

Domain Interest:

Strong passion for applying frontier AI and agentic science to AI for Drug Discovery (AI4DD), biology, and chemistry.

Preferred Qualifications:

• Inference-Time Optimization: Deep expertise in LLM serving, test-time compute, sampling/search strategies, model routing, batching, caching, and latency/cost/quality tradeoffs.
• Scientific Context: Experience working with molecular modalities (e.g., protein sequences, chemical graphs, and structured molecular data).
• Open Source: A public portfolio of significant technical contributions to open-source ML, systems, or MLOps libraries.

Onsite presence on our campus is expected in compliance with Roche/Genentech policy.

Relocation benefits are not available for this job posting.

The expected salary range for this position based on the primary location of New York is $192,500 - $357,500.

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 Genentech 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.

Who We Are

Genentech, a member of the Roche group and founder of the biotechnology industry, is dedicated to pursuing groundbreaking science to discover and develop medicines for people with serious and life‑threatening diseases. To solve the world's most complex health challenges, we ask bigger questions that challenge our industry and the boundaries of science to transform society. Our transformational discoveries include the first targeted antibody for cancer and the first medicine for primary progressive multiple sclerosis.

The next step is yours.

Genentech 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.