Teamworks
Senior Technical Product Manager, AI
$185K
United States · Remote · Manager · Full-time · Posted Oct 26, 2026
- Experience
- 5+ years of software engineering or technical PM experience building production systems; may be before or alongside moving into product. Preferred: three or more years as a backend, platform, data, or distributed-systems engineer.
- Visa sponsorship
- This role is not eligible for sponsorship at this time.
About the job
Own and scale Ask Teamworks and shared AI platform capabilities, taking AI agents from beta to general availability and enabling trusted cross-product workflows across Teamworks’ sports products.
What you'll do
- Lead rollout and adoption of AI agents across products, partnering with product teams to move capabilities from beta to general availability.
- Define shared platform capabilities, technical standards, integrations, and AI quality evaluation and release practices.
- Develop cross-product agent workflows and actions, and collaborate on usage measurement, customer allowances, packaging, and responsible data handling.
What they ask for
- 5+ years of software engineering or technical PM experience building production systems.
- Track record of shipping an LLM-based or agentic product to real users with evidence of adoption or customer value.
- Product ownership experience in B2B SaaS, vertical AI, platform, or developer products, coordinating across products and functions.
- Hands-on AI evaluation experience, including datasets, rubrics, regression testing, human review, and release thresholds.
Nice to have
- Three or more years as a backend, platform, data, or distributed-systems engineer; recent hands-on work with MCP, agent frameworks such as Mastra, or self-built prototypes.
- Experience with sensitive-data permissions or usage-based pricing, or subject-matter expertise in American football, soccer, hockey, or basketball.
Pay
$185K (base salary; posting does not specify currency code)
Tools
LLM, MCP, Mastra
AI focus
Lead AI agents and shared platform development, including cross-product agentic workflows, product-embedded actions, evaluation datasets and testing, and rollout from beta to general availability.
Model-written summary · the original posting is authoritative