Technical Program Lead, AI Delivery

San Francisco Service Operations – Delivery & Operations Full Time Hybrid About SuperAnnotate SuperAnnotate helps the world’s leading AI teams build responsible, next-generation models powered by high-quality human data. It is a fast-growing Series B startup bridging the gap between advanced AI innovation and the data that drives it. With a global network of expert specialists, scalable managed operations, precise talent matching and full project transparency, SuperAnnotate ensures unmatched data quality at scale, trusted by innovators such as Databricks and ServiceNow and backed by NVIDIA, Dell Technologies Capital, Databricks Ventures, Cox Enterprises and Lionel Messi’s Play Time VC. The Impact You’ll Make 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. The role involves working directly with researchers and engineering teams, debugging messy data workflows and taking meaningful ownership at the intersection of customer delivery, systems design and AI model quality. The operator will run the largest LLM and Gen AI data programs, acting as a trusted technical resource to client researchers, diagnosing data quality issues and reallocating resources under tight deadlines. While not directly training models, the candidate must have enough technical depth to inspect data, debug pipelines, understand evaluation trade‑offs and communicate credibly with ML researchers and engineers. What You’ll Do 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, ensuring delivery quality, client trust and program success. Serve as the client’s main technical point of contact. Build and maintain trusted relationships with researchers and engineering stakeholders, bringing 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 systems that enable delivery. Architect data pipelines, quality frameworks and review infrastructure that allow the team to 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, catching problems before they become crises. Lead and develop your team. Recruit, mentor and performance‑manage the operations and subject‑matter experts on your programs, setting the bar and building toward a team that can scale. Identify scope expansion opportunities. Partner with Go‑to‑Market to turn strong delivery into expanded engagements, understanding clients’ broader AI data challenges well enough to propose next steps. Keep leadership and clients informed. Develop reporting cadences and executive briefings that give an honest, clear picture of project health, risks and outcomes. What You’ll Bring 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: managed cross‑functional workstreams with real accountability and can point to what was 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: comfortable in a room with a CTO or VP and can represent a complex technical project clearly and honestly. Sharp operational problem‑solving: diagnose issues structurally, propose solutions with clear trade‑offs and move quickly from analysis to action. Financial and resource fluency: understand project margins, build resource plans and consider 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 Have 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. Compensation $170,000 – $210,000 a year. In addition to the annual base salary, employees are eligible for an annual bonus paid out quarterly. Equal Opportunity We are an equal‑opportunity employer and value diversity at our company. We make an effort to reflect the many experiences and identities of the outside world, treating each other with fairness and without bias. Every day we foster an environment where people of all backgrounds not only belong, but excel and succeed. We offer equal opportunity regardless of sex, sexual orientation, national origin, color, race, age, marital status, disability, gender identity, veteran status and more. We may use artificial intelligence tools to support parts of the hiring process, such as reviewing applications, analysing resumes or assessing responses, but final hiring decisions are ultimately made by humans. #J-18808-Ljbffr Berkeley SkyDeck Decal