Teams at Genentech

Leads research and engineering for internal reasoning large language models supporting drug discovery and biomolecular design. Responsibilities include setting model architecture and training strategy, improving scientific reasoning capabilities, translating biological and chemical knowledge into ML objectives, building scalable distributed training systems, establishing evaluation benchmarks, and transitioning research prototypes into production. The role also provides technical mentorship and requires strong research excellence, software engineering, and large-scale deep learning expertise.

Lead engineering for foundation models, autonomous agents, and agentic systems supporting AI-driven drug discovery. Architect scalable distributed training and inference infrastructure, productionize Python and deep learning systems, establish MLOps and AgentOps practices, and define technical strategy. Build agent memory, orchestration, scientific data integrations, observability, evaluation, CI/CD, and deployment systems. Partner with ML scientists and domain experts to translate research into reliable, enterprise-grade platforms.

Conduct analytical testing of stability samples for biotherapeutics, apply thermo-kinetic modeling, evaluate biophysical characterization techniques, and generate and document high-quality experimental data. The intern will independently design, execute, and analyze laboratory experiments using protein characterization and stability-study methods while collaborating with pharmaceutical development scientists.