Strategic Projects Lead (Technical)

The Impact You'll Make

About SuperAnnotate SuperAnnotate helps the world’s leading AI teams build responsible, next-generation models powered by high-quality human data. We’re a fast-growing Series B startup bridging the gap between advanced AI innovation and the data that drives it. Our global network of expert specialists, scalable managed operations, precise talent matching, and full project transparency ensure unmatched data quality at scale. Trusted by innovators like Databricks and ServiceNow - and backed by NVIDIA, Dell Technologies Capital, Databricks Ventures, Cox Enterprises, and Lionel Messi’s Play Time VC - SuperAnnotate is proud to be the top-ranked AI data company on G2 for multiple consecutive years, including 2025. Why SuperAnnotate This role sits at the center of what SuperAnnotate does and what the AI industry needs most right now. You’ll work on problems that matter, with clients who are building the systems that will define the next decade of AI. You’ll have real ownership, real visibility, and a direct line to leadership. Only shortlisted candidates will be contacted for an interview! Equal Opportunity We are an equal-opportunity employer and value diversity at our company. At SuperAnnotate diversity means to us making an effort to reflect the many experiences and identities of the outside world, and treating each other with fairness and without bias. Every day we foster an environment where people of all backgrounds not only belong, but excel to succeed as a company and grow together. We offer equal opportunity regardless of sex, sexual orientation, national origin, color, race, age, marital status, disability, gender identity, veterans and more.

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.