What you'll be doing

• Architect LLM-integrated systems focusing on scalability, security, and user experience.
• Enable telemetry, observability, governance, and cost management; build platform components and tools.
• Implement advanced LLM context management, tool-use, skill tooling, and retrieval-augmented generation (RAG) to enhance research workflows.
• Build evaluation frameworks to ensure model reliability and safety, maintaining evals for diverse tasks.
• Develop internal frameworks and IDE integrations for agentic coding and workflows at scale.
• Support fine-tuning and reinforcement learning efforts in collaboration with AI researchers for biological and chemical data optimization.
• Collaborate strategically with researchers and business teams to translate scientific needs into AI technical solutions and unlock LLM-based use cases.

Requirements

• Strong software engineering skills in Python focused on production-grade, maintainable systems.
• Ability to architect and maintain complex cloud systems with infrastructure as code (IaC), emphasizing security, UX, and scalability.
• Deep understanding of LLM training, internal workings, and limitations.
• Experience with LLM serving stacks (e.g., vLLM) for deploying open weight models internally.
• Proficiency in evaluating probabilistic ML systems and managing model tool use, contexts, and iterative processes.
• First-principles thinking with a pragmatic approach to problem-solving and technology selection.
• Excellent communication and stakeholder management skills, capable of explaining technical risks to non-experts.

Nice to haves

• Prior experience building and scaling large language model-centered systems.
• Experience setting up hardware or orchestration for machine learning infrastructure.
• Familiarity with Google Cloud Platform (GCP) for building and deploying systems.
• Interest or experience in biology, chemistry, drug discovery, or applying AI to scientific discovery.
• Knowledge of current agentic tooling and developer frameworks for fast-evolving technology.

Perks and benefits

• Collaborative and interdisciplinary culture focused on innovation and impactful scientific discovery.
• Hybrid working model requiring presence in the office three days a week to foster knowledge sharing and strong relationships.
• Commitment to diversity, equity, and inclusion, providing a supportive environment where all employees can thrive.
• Opportunity to work on cutting-edge AI systems aiming to transform drug discovery and human health.