Technical Program Lead, AI Delivery

SuperAnnotate AI•San Francisco, CA

15h•Hybrid

About The Position

We're looking for a technical operator who can own SuperAnnotate's most complex AI data programs end-to-end, from scoping and data design through delivery, quality, and client-facing problem solving. This role is for someone who likes working directly with researchers and engineering teams, can debug messy data workflows, and wants meaningful ownership at the intersection of customer delivery, systems design, and AI model quality. You'll run the largest LLM and Gen AI data programs at SuperAnnotate, acting as a trusted technical resource to client researchers, diagnosing data quality issues, and reallocating resources under tight deadlines. You won't be training models yourself, but you'll need enough technical depth to inspect data, debug workflows, understand evaluation tradeoffs, and speak credibly with ML researchers and engineers.

Requirements

4+ years running complex technical projects or operations in client-facing roles, with at least some of that time in a high-growth startup environment.
A track record of owning outcomes: you've managed cross-functional workstreams with real accountability, and you can point to what you delivered and how.
Coding ability and analytical depth: solid Python or SQL skills, alongside depth in machine learning, statistics, or data science, applied to real debugging, analysis, or tooling work.
Strong senior stakeholder communication: you're comfortable in a room with a CTO or VP and can represent a complex technical project clearly and honestly.
Sharp operational problem-solving: you diagnose issues structurally, propose solutions with clear tradeoffs, and move quickly from analysis to action.
Financial and resource fluency: you understand project margins, can build resource plans, and think about the business impact of operational decisions.
Working knowledge of the Gen AI landscape: familiarity with how LLMs are trained and evaluated, and what drives data quality at each stage.
Bachelor's degree in Statistics, Data Science, Machine Learning, Computer Science, Mathematics, Engineering, or a related quantitative field, or equivalent experience.

Nice To Haves

Experience building or managing large distributed contributor or expert workforces
Background in management consulting, investment banking, or data-intensive technical services
MBA or equivalent advanced degree

Responsibilities

Own project delivery end-to-end. Lead LLM and Gen AI data engagements from initial scoping through final delivery, including use case definition, resource planning, quality oversight, and client sign-off. You'll be accountable for delivery quality, client trust, and program success.
Be the client's main technical point of contact. Build and maintain trusted relationships with researchers and engineering stakeholders. Bring transparency, good judgment, and a solutions orientation to every interaction.
Work hands-on with data and code. Write Python and SQL to inspect data, debug pipeline and schema issues, and spot quality problems before they reach the client. Build lightweight tooling and automate QA or reporting where it saves the team time.
Design and build the systems that make delivery possible. Architect data pipelines, quality frameworks, and review infrastructure that let your team execute at scale. Instrument the right KPIs, catch regressions early, and refine processes as program needs evolve.
Drive operational discipline across workstreams. Manage timelines, staffing plans, and delivery quality across multiple concurrent projects. Catch problems before they become crises.
Lead and develop your team. Recruit, mentor, and performance-manage the operations and subject matter experts on your programs. Set the bar and build toward a team that can scale.
Identify scope expansion opportunities. Partner with Go-to-Market to turn strong delivery into expanded engagements. Understand your clients' broader AI data challenges well enough to propose what's next.
Keep leadership and clients informed. Develop reporting cadences and executive briefings that give an honest, clear picture of project health, risks, and outcomes.