Note: The job is a remote job and is open to candidates in USA. Fetch is seeking a Senior Product Manager to lead its internal Agentic Platform. The role owns the platform portfolio, agent development lifecycle, internal user experience, adoption, evaluation, governance, and cross-functional delivery of capabilities that help teams build and operate AI agents in production.
Responsibilities
- Own the product vision, strategy, and roadmap for Fetch's internal Agentic Platform: agent runtime and orchestration, context and memory, model routing and gateways, evaluation, observability, and governance
- Treat the platform as a product: prioritize capabilities by what agent teams actually need next, sequence the roadmap against real agent launches, and measure success by what ships on top of the platform
- Design the platform's human-in-the-loop and multi-agent primitives — the shared building blocks teams use to define roles, handoffs, review points, escalation paths, and feedback loops between agents and the people who oversee them
- Partner with Fetch's developer productivity and infrastructure teams so the platform shows up to builders as one coherent experience — shared capabilities and paved paths rather than one-off solutions
- Extend what works: as capabilities prove out with one agent team, generalize them into shared primitives other teams can adopt — building on the automation muscle Fetch has already developed
- Define success metrics tied to platform outcomes — time from agent idea to production, adoption across teams, reliability, and quality and cost per agent task — and drive experimentation to improve them
- Look around the corner: anticipate where the agent ecosystem is heading — MCP, agent-to-agent interop, agent identity and delegated access — and position the platform so Fetch's agents can safely work with agents and surfaces we don't control
- Own the ADLC end to end: define how a team at Fetch takes an agent from idea through evaluation, launch, and production operations — and make that path faster, safer, and more repeatable with every release
- Build and operationalize evaluation capabilities every agent team can use — offline evals and golden datasets before launch, production quality monitoring after
- Define the shared building blocks of the lifecycle: prompt, schema, and tool registries; a unified programmatic interface to the platform (CLI, API, MCP); and onboarding checks for new agents and tools
- Design governance that enables rather than blocks: least-privilege agent access, guardrails, and review points that let teams ship quickly and trust what they ship
- Drive continuous improvement loops: prompt and context engineering, model updates, and tool design informed by eval results and real user feedback
- Establish observability and performance metrics for agent effectiveness, reliability, and cost across the portfolio
- Serve as the embedded researcher and voice of the teams who build on the platform: engineers, product managers, data scientists, and the operations teams who depend on what they ship
- Run structured discovery — interviews, ride-alongs, workflow shadowing — and translate it into journey maps of the agent development lifecycle, friction inventories, and prioritized platform opportunities
- Decide where the platform should be opinionated and where it should stay out of the way — defining the defaults, escape hatches, and override mechanisms builders need to trust the platform with production workloads
- Own adoption as a first-class product outcome: onboarding, enablement, feedback channels, and iteration until the platform is what teams reach for by default — not just tried once
- Close the loop between builders and the roadmap, ensuring friction, escalations, and workarounds feed directly into platform improvements and evaluation sets
- Run the program with rigor: crisp documents, roadmaps, and status communication; dependencies tracked across many teams; nothing dropped
- Build alignment across the many stakeholders this work touches — engineering, data, security, design, and the agent teams building on the platform
- Translate complex technical concepts into clear business value for leadership — and translate builder and operator realities back into technical requirements for engineering
- Communicate progress, risks, and tradeoffs clearly to senior leadership
- Operate with a high degree of autonomy in a space where the problems, tools, and org structures are all still taking shape
Skills
- 4+ years of Product Management experience, with meaningful time spent on platform or infrastructure products, developer tools, or AI/ML-powered products
- Experience shipping AI/LLM-powered features to real users, with working fluency in LLMs, prompt engineering, evaluation frameworks, and agentic loops
- Demonstrated discovery and research skills — comfortable running your own user interviews, shadowing sessions, and workflow mapping, and turning them into product requirements
- Strong systems thinking and process design capabilities, with a track record of identifying and redesigning complex workflows
- Exceptional organizational skills: able to run multi-team programs, manage dependencies, and keep many stakeholder groups aligned simultaneously
- Strong analytical mindset with experience defining metrics, running experiments, and driving measurable outcomes
- Proven ability to work cross-functionally and influence stakeholders across technical and non-technical teams — especially engineering and data science
- Excellent written and verbal communication skills, with the ability to clearly articulate complex ideas to diverse audiences
- Comfort operating in fast-paced, ambiguous environments where problem spaces are not yet fully defined
- Experience building internal platforms or developer-facing products where your users are other builders — including measurable adoption, enablement, and change management
- Experience working with AI agents, multi-agent systems, or conversational AI in production environments
- Familiarity with the emerging agent infrastructure ecosystem: agent frameworks, MCP and agent-to-agent protocols, model gateways, and agent observability tooling
- Familiarity with AI evaluation frameworks, prompt engineering, and quality measurement for generative AI products
- Experience designing human-in-the-loop systems where AI output is reviewed, corrected, or approved by expert operators
- Formal or informal UX research experience: study design, contextual inquiry, usability testing, or workflow analysis
- Background in marketplace, rewards, loyalty, or retail media businesses
Benefits
- Equity: We offer full-time employees equity in Fetch, so that everyone can benefit from Fetch’s growth.
- 401k Match: Dollar-for-dollar match up to 4%.
- Comprehensive medical, dental and vision plans for everyone, including pets.
- Continuing Education: Fetch provides ten thousand per year in education reimbursement.
- Employee Resource Groups: Employee-led groups centered around fostering a diverse and inclusive workplace through events, dialogue and advocacy.
- Flexible PTO, 9 paid holidays, and a year-end week-long break.
- 20 weeks of paid parental leave for primary caregivers, 14 weeks for secondary caregivers, and a flexible return-to-work schedule.
- A one-time $2,000 incentive for employees welcoming new family members to assist with childcare, clothing, diapers and more.
- Flexible Work Environment: Collaborate with your team in one of our offices, or work fully remotely from anywhere in the US (applicable for most roles).
- Hardware and software provided to ensure remote employees are equally equipped to get the job done from home.
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