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Salary: USD 296,000 - 444,000 / annual
Snorkel AI is seeking a Strategic Delivery Lead to own the operational and financial performance of a business pod focused on AI data products and services. This is a hybrid role (3 days/week in office) based in San Francisco, CA or New York City, NY.
You will serve as the senior operator accountable for translating the pod's strategic ambitions into reliably delivered, high-quality work. You will directly manage a team of Strategic Project Leads and lead through influence across GTM, Research, Delivery, Product, Engineering, and Expert AI Supply functions. Your responsibilities span a portfolio including samples, off-the-shelf datasets, proprietary data products, and bespoke customer engagements.
Key responsibilities include:
- Own the pod's P&L, margin performance, and operating economics. Track cost-to-deliver, gross margin, capacity, and staffing decisions that impact financial health. Provide cost and delivery inputs that inform pricing decisions and represent performance in weekly business reviews.
- Execute the pod's four-quarter roadmap by partnering with Product and Research to translate strategy into actionable delivery plans, mobilizing cross-functional teams, and ensuring launches meet quality, speed, and financial targets.
- Manage and develop a team of Strategic Project Leads, establishing a culture of high performance and accountability.
- Serve as the connective tissue between GTM and Delivery, maintaining visibility into the commercial pipeline, supporting pre-sales and marketing teams, and communicating delivery plans, capacity, risks, and tradeoffs.
- Ensure every project—across all engagement types—is delivered on time, to the highest quality standards, and against expected financial returns.
- Mobilize the right expertise at the right time by negotiating for capacity and driving alignment across teams that do not report directly to you.
- Partner with Delivery and Expert AI Supply leadership to build and optimize the workforce plan, ensuring projects are staffed with appropriate expertise and capacity is planned strategically.
- Obsess over quality by diving into data, identifying where quality breaks down, and forcing necessary conversations and actions regardless of direct authority.
- Actively manage and improve margin by challenging staffing models, production pipelines, and other cost drivers to deliver measurable improvements.
- Convert recurring failures into systemic fixes by addressing root causes in operating models, tooling, and processes rather than solving problems one project at a time.
- Establish the pod playbook—the rhythms, processes, roles, and best practices that make the model repeatable for future pods.
- Share learnings and proven practices across the broader organization so all pods benefit from collective insights.
Requirements:
- Ownership of a P&L or business unit: you have carried a number, made pricing and cost tradeoffs, and lived with the results. Consulting-style project leadership without commercial ownership does not qualify.
- Real depth in a technical domain such as software engineering, computer use and agents, or an adjacent frontier area. You do not need to be a researcher or write code, but you must be able to hold your own in technical discussions, assess data quality, and know when to escalate to experts.
- Track record of building rather than administering. Founders, early operators, and people who have built something from nothing tend to excel. Substance of accomplishment matters more than headcount managed.
- Commercial judgment with customers: ability to run technical discovery conversations, scope engagements, and hold hard margin or timeline conversations without escalation.
- Comfort with ambiguity and working through influence with peers who are not your direct reports. Most work will be accomplished through people outside your direct authority.
- Seniority: typically 8+ years with meaningful time at the level described above, though substance is valued over tenure.
- Prior people management experience; preference for 3+ years.
Bonus qualifications: experience in AI/ML data, evals, or benchmarks; having built a function or business line inside a fast-scaling company; founder or early-employee background in a technical product.
