Senior Machine Learning Engineer, AI Agents for Science, AI for Drug Discovery

We are seeking a Senior Machine Learning Engineer to join the Foundation Model team to build the platform for autonomous scientific agents to automate and accelerate drug discovery. The team focuses on developing and operating the foundational platform that transforms scientific knowledge and actions from thousands of world-class scientists into sharable, reusable tools, workflows, and agents, reshaping how drug discovery operates with large-scale in-house scientific use cases.

Requirements

• Design and build the distributed backend infrastructure for multi-agent systems, managing state, orchestration, and execution across our compute clusters.
• Implement and standardize tool interfaces using the Model Context Protocol (MCP) to expose internal scientific packages (chemistry, biology, and informatics tools) 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 for internal research teams.
• 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 for the broader team.
• 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 the observations.
• Design and perform training and evaluation of the backbone Large Language Models (LLMs) for improved scientific agentic performance

Benefits

• #ComputationCoE
• #tech4lifeComputationalScience
• #tech4lifeAI