Agentic AI Lead – Protein Design & Molecular Engineering

Job description

Career Category Engineering Job Description Position Overview The GCF5 Sr Machine Learning Engineer is the senior technical leader for the Agentic & ML Platform pillar. They define and socialize platform standards and patterns, lead multi-team delivery, mentor GCF4 engineers, and translate scientific needs into scalable ML/agentic platform designs. They own pillar-level adoption, reliability, and SLA/SLO outcomes, and influence cross-team engineering quality. This role reports to the GCF7 leader and partners closely with peer GCF5 domain leads across SCIP to ensure cohesive, scalable platform evolution. Core Responsibilities Own the ML and agentic platform technical roadmap within SCIP. Design and operationalize reusable ML/agentic infrastructure components enabling repeatable deployment. Define evaluation harnesses and model release gates. Establish monitoring, rollback, and observability practices for production ML systems. Implement guardrails and operational controls for safe agentic workflows. Define reproducibility standards and artifact versioning practices. Lead architecture reviews for ML platform evolution. Mentor engineers and elevate ML engineering rigor. Partner with research stakeholders to translate AI use cases into scalable platform capabilities. Core Competencies Deep expertise in the assigned pillar (Agentic & ML Platform) (Agentic‑ML) with evidence of standard‑setting and reuse. Systems design at scale (ML); performance, security, and observability fundamentals. Product/engineering thinking: road mapping, prioritization, and outcome‑oriented delivery. Stakeholder influence across science, engineering, and governance forums; crisp written/verbal communication. Core Success Measures Adoption rate of standardized ML platform components. Evaluation coverage across supported ML use cases. Reduction in model regressions and production ML incidents. Time-to-deploy new ML use cases. Reproducibility rate of experiments and deployments. Reduction in safe-use escalations. Key Relationships Collaborates with GCF6 Group Lead and cross‑functional leaders (R&D/PD/Dev). Mentors and develops GCF4 Data and Software Engineers, partners with platform, data, ML, and research teams. Interfaces with governance (architecture, security, compliance) and vendor/partner teams. Decision Authority Approve designs within the pillar; define and waive standards/patterns with rationale. Recommend buy‑vs‑build; commit pillar resources to meet SLAs/SLOs; escalate risks. Prioritize pillar backlog and roadmap in alignment with strategy and OKRs. Qualifications Basic Qualifications: BS+8 / MS+6 / PhD in CS/Engineering/Data disciplines. Demonstrated production delivery experience in ML/agentic platforms at scale. Demonstrated literacy in a relevant scientific domain (e.g., biology, chemistry, therapeutic discovery). Preferred Qualifications: Depth in the assigned pillar (Agentic & ML Platform). Kubernetes and continuous integration/continuous delivery (CI/CD) at scale; observability, performance tuning, and security-by-design. Evidence of standard‑setting and cross‑team influence; mentoring experience. .