Artificial Intelligence

• Lead the Proteome Editor team in building and running a rapid, repeatable design-test-learn pipeline focused on proteome editor screening
• Design and oversee library screening and degrader evaluation experiments to generate high-quality, model-ready datasets
• Partner with machine learning colleagues to define next targets, curate data, and implement active-learning strategies
• Optimize flywheel performance by improving turnaround time, throughput, cost, and per-cycle model lift
• Identify and remove experimental bottlenecks
• Coordinate with other flywheel leads and AI BioDesign stakeholders to align priorities, timelines, and dependencies
• Mentor and manage a high-performing team
• Foster a collaborative culture and clear communication
• Present scientific results and complex concepts internally in talks and progress reports
• Present results to the external community at conferences and in publications

Scientist III – Proteome Editor Design, AI BioDesign

• Lead complex AI infrastructure and data center deployment programs from planning through production handoff
• Coordinate site readiness, racks, cabling, power, cooling, networking, storage, and GPU infrastructure
• Translate technical requirements into deployment plans, milestones, readiness criteria, and deliverables
• Partner with engineering, infrastructure, facilities, operations, supply chain, OEMs, contractors, and vendors
• Own program schedules, dependencies, critical paths, risks, issues, actions, key decisions, and RAID
• Drive commissioning, integrated systems testing, burn-in, validation, punch-list resolution, and operational readiness
• Identify blockers and keep complex deployments moving
• Provide updates to technical and executive leadership on progress, risks, trade-offs, and decisions
• Establish repeatable processes for deploying AI infrastructure efficiently and reliably at scale
• Lead work across GPU infrastructure, high-performance networking, AI/HPC storage, high-density infrastructure, cluster bring-up, performance acceptance, and production operations as applicable

Technical Program Leader – AI Infrastructure

• Lead the Data & AI team’s measurement, learning, and evaluation function as a core member of the Data & AI Enablement Hub
• Define and track strategy and portfolio progress through target-setting, performance indicators, and evaluation design
• Lead evaluations to test hypotheses and assumptions underlying team investments
• Design and build interactive performance dashboards
• Develop and evolve predictive and evaluative impact accounting frameworks
• Apply effect-size extraction, scenario analysis, and cost-effectiveness approaches to support investment decisions
• Analyze, synthesize, and communicate findings from evaluations, research, and pilots
• Provide evaluation design, hypothesis testing, and progress reporting for grants and investments
• Consult with program officers, grantees, and partners to ensure grants produce data and evidence needed to measure strategy progress
• Scope and manage evaluations with external partners
• Facilitate team strategy learning and share insights, data, and recommendations across teams
• Leverage AI tools and digital technologies to streamline MLE workflows and improve reporting and evidence synthesis
• Engage in division- and foundation-wide MLE learning communities
• Represent the team’s MLE approach in cross-foundation forums
• Apply an equity-centered lens to evaluation design and evidence synthesis
• Support inclusive culture and escalate issues to appropriate stakeholders

Artificial Intelligence jobs in Washington

Scientist III – Proteome Editor Design, AI BioDesign

Technical Program Leader – AI Infrastructure

Senior Officer, Measurement, Learning, and Evaluation – US Program Data & AI