Senior Machine Learning Engineer

2w2 weeks ago

Genentech

New York City, US · Full-time · $160,100 – $167,400

About this role

At Roche's AI for Drug Discovery group, we are revolutionizing drug discovery with cutting-edge machine learning techniques. The Foundation Models team builds large language models and agent platforms enabling next-generation scientific applications across the drug-discovery pipeline. We seek a Senior Machine Learning Engineer to build the platform for autonomous scientific agents automating drug discovery.

You will partner with Machine Learning Scientists to engineer distributed systems allowing models to plan workflows, interact with scientific software, and execute complex tasks. Lead the design and implementation of core infrastructure bridging model inference and experimental data generation. Work with drug discovery scientists to deploy the system to real processes.

Join the Computational Sciences Center of Excellence harnessing AI to assist scientists in pRED and gRED for innovative medicines. Transform scientific knowledge and actions from world-class scientists into sharable tools, workflows, and agents. Operate one of the largest scientific agent platforms with large-scale in-house use cases.

Drive engineering excellence by defining standards, leading code reviews, and building reusable Python libraries. Collaborate with computational scientists on designing targeted agents for drug discovery. Explore frontier research on agentic use in scientific scenarios and publish observations.

Requirements

Experience engineering distributed systems for multi-agent platforms and model orchestration
Proficiency implementing tool interfaces and APIs for scientific software in chemistry, biology, and informatics
Expertise in cloud-native deployment, event-driven architectures, and production scaling
Skills in performance optimization including RAG, caching, and parallel task execution
Strong Python development for reusable libraries and engineering standards
Background in ML engineering for LLMs and autonomous agent systems
Ability to collaborate with domain experts in drug discovery and computational sciences
Interest in frontier research on scientific agents with publication experience

Responsibilities

Design and build the distributed backend infrastructure for multi-agent systems, managing state, orchestration, and execution across compute clusters
Implement and standardize tool interfaces using the Model Context Protocol (MCP) to expose internal scientific packages as executable actions for models
Engineer robust APIs and event-driven architectures to integrate agent workflows with experimental data pipelines and execution environments
Deploy and scale agentic systems in production using modern cloud-native patterns, ensuring high availability and low-latency access
Optimize system performance, including efficient context management (RAG), caching, and parallel execution of scientific tasks
Drive engineering excellence by defining software standards, leading code reviews, and building reusable Python libraries
Collaborate closely with computational scientists and subject matter experts on designing and evaluating targeted agents for drug discovery
Explore frontier research topics related to agentic use in scientific scenarios and publish observations