The Impact You'll Make
We’re looking for a skilled, technical operator who can take full ownership of our most complex, high-value client engagements.
As a Strategic Projects Lead, you’ll run the end-to-end delivery of SuperAnnotate’s largest LLM and Gen AI data programs. This includes everything from scoping data collection workflows with key clients, acting as a trusted resource to researchers, diagnosing data quality issues, and reallocating resources under a tight deadline. The role touches everything: strategy, execution, client relationships, team development, and process design.
You’ll work directly with researchers, data teams, and other key stakeholders at some of the most advanced AI organizations in the world. You'll be trusted to represent SuperAnnotate at the highest level, including on technical tradeoffs, and to grow those relationships through execution and management excellence.
You don't need to be building models, but you do need to be technical enough to understand how they're trained, evaluated, and improved. That means hands-on comfort with data, code, and pipelines, and the ability to engage credibly with client engineering and research teams on LLM data challenges and evaluation approaches.
This is a full-time, hybrid position based in San Francisco.
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. The project succeeds or fails on your watch.
Be the client’s main point of contact: Build and maintain trusted relationships with key stakeholders. Bring transparency, good judgment, and a solutions orientation to every interaction.
Drive operational discipline across workstreams: Manage timelines, staffing plans, and delivery quality across multiple concurrent projects. Catch problems before they become crises and fix them before they reach the client.
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 continuously refine processes as program needs evolve.
Lead and develop your team: Recruit, mentor, and performance-manage the operations and subject matter experts. Set the bar, support the people, 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.
What You'll Bring
4+ years of experience 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: You have solid coding skills (Python, SQL, or similar) alongside depth in machine learning, statistics, or data science.
Strong senior stakeholder communication: You’re comfortable in a room with a CTO or VP and can represent a complex project clearly, confidently, 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.
A working knowledge of the Gen AI landscape: Familiarity with how LLMs are trained and evaluated, and what drives data quality at each stage, so you can guide your team’s approach and speak knowledgeably with client research teams.
Bachelor's degree or higher in Statistics, Data Science, Machine Learning, Computer Science, Mathematics, Engineering, or a related quantitative field. CS, Math, and Engineering degrees should include meaningful ML or applied data science exposure.
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.
