OpenAI· Model Deployment for Business· San Francisco
Technical Deployment Lead, Life Sciences
Comp$198K – $335K
Classified Tasks (25)
Automate 0%Augment 56%Human-Only 44%
Augment (14)
AI assists, human decides
Translate business objectives into technical roadmaps with milestones, dependencies, and acceptance criteria
technical
Own the technical delivery plan for multiple interdependent workstreams
technical
Track and drive delivery progress across OpenAI and customer teams
operational
Shape tools and integrations to meet customer workflow requirements
technical
Translate customer requirements into detailed delivery plans
operational
Ensure components and deliverables ship on time
operational
Manage project dependencies across internal and external teams
operational
Share field insights with Product and Research to influence product roadmaps and priorities
communication
Extract and codify reusable solution patterns and evaluation frameworks from field deployments
analytical
Package and communicate field signals to improve products and models
communication
Define value cases and ROI hypotheses for customer deployments
analytical
Establish baselines and KPIs to measure deployment impact
analytical
Run pre-deployment and post-deployment measurement and analysis
analytical
Report deployment outcomes and ROI to executive sponsors
communication
Human-Only (11)
Requires human judgment
Define delivery approach for complex systems to Life Sciences customers
operational
Own end-to-end building, shipping, and adoption of deployed systems
leadership
Coordinate and run day-to-day engineering execution across OpenAI FDEs, Researchers, and Customer Engineers
operational
Unblock progress and maintain proper sequencing of workstreams
operational
Make real-time trade-offs on scope and priority to protect the critical path
leadership
Embed with Life Sciences customer teams to map workflows and define success criteria
communication
Lead onboarding, adoption, and change management for deployed solutions
leadership
Make sequencing decisions for prototype, MVP, and scale phases
technical
Drive 0→1 prototypes through MVP development and scaling
technical
Partner with Product and Research to align platform components and research workstreams to deployment timelines
operational
Design engagement strategies and operating models for Life Sciences customers
operational
Job description
Technical Deployment Lead, Life Sciences | OpenAI Careers ## Technical Deployment Lead, Life Sciences Model Deployment for Business - San Francisco Apply now(opens in a new window) **About the team** OpenAI’s Forward Deployed Engineering team partners with customers to turn research breakthroughs into production systems. We operate at the intersection of customer delivery and core platform development. **About the Role** As a Technical Deployment Lead (TDL) for Life Sciences, you will define how OpenAI delivers complex systems to customers. You will own how they are built, shipped, and adopted. You’ll translate business outcomes into a technical plan, run day-to-day execution across FDEs, Researchers, and Customer Engineers, and partner with customer teams to ensure delivery supports their goals. You will focus on the Life Sciences vertical, partnering with pharmaceutical companies, clinical research organizations, and other data and services providers to deploy next-generation AI capabilities across their drug discovery, development, and operations. You will own delivery end-to-end: embedding with Life Sciences customers to map workflows and success criteria, ensuring components ship on time, and leading readiness and change management for adoption. You’ll track progress, manage dependencies, make sequencing decisions, and drive 0→1 prototypes through MVP and scale. You will also share field insights with Product and Research to guide roadmap and priorities. Success will be measured first and foremost by impact - deployments that deliver measurable value against customer goals, drive adoption, and become critical to their workflows. Additional measures of success include delivery reliability (milestones hit, low reopen/churn), operating leverage (patterns reused across deployments), judgment under pressure, and product impact (field signal that shifts roadmaps/architectures). This is a high-trust, high-autonomy role. Success requires deep technical project management expertise, extreme ownership of outcomes, and an ability to immerse in customer workflows and partner with customer teams to solve complex engineering problems at pace. This role is based in San Francisco. We use a hybrid work model of 3 days in the office per week. We offer relocation assistance. Travel up to 50% is required. **In this role, you will:** * **Own the technical delivery plan** for multiple interdependent workstreams. Translate business objectives into a roadmap with milestones, dependencies, and acceptance criteria. * **Run day-to-day engineering execution.** Track and drive delivery across OpenAI FDE and customer teams. Keep progress unblocked and sequenced. Make real-time trade-offs on scope and priority to protect the critical path. * **Embed with customer teams to land production deployments and drive adoption.** Map workflows, shape tools/integrations, and translate requirements into a delivery plan. Lead onboarding, adoption, and change management. * **Partner with Product and Research** so platform components and research workstreams land in time to support deployment goals. * **Codify solution patterns and evals.** Extract reusable patterns and package field signals to improve product and models. * **Own value cases and ROI.** Set impact hypotheses, baselines, and KPIs; run pre-/post-deployment measurement and report to exec sponsors. * **Shape how we engage Life Sciences customers** **You’ll thrive in this role if you:** * Bring 7+ years of customer‑facing technical delivery leadership. * Have expertise in Life Sciences * Track record of successfully leading large, complex, high-stakes customer engagements where customer outcomes depended on tight coordination and fast decision making, ideally involving AI. * Excel in high ambiguity environments. Know how to simplify complex and dynamic work. * Move fluidly between system level understanding and execution level detail; can dive into