Principal, Technical Product Manager - AI Frontiers Palo Alto, California,

Principal, Technical Product Manager - AI Frontiers

Upwork Inc.'s (Nasdaq: UPWK) family of companies connects businesses with global, AI-enabled talent across every contingent work type including freelance, fractional, and payrolled. This portfolio includes the Upwork Marketplace, which connects businesses with on-demand access to highly skilled talent across the globe, and Lifted, which provides a purpose-built solution for enterprise organizations to source, contract, manage, and pay talent across the full spectrum of contingent work.

From Fortune 100 enterprises to entrepreneurs, businesses rely on Upwork Inc. to find and hire expert talent, leverage AI-powered work solutions, and drive business transformation. With access to professionals spanning more than 10,000 skills across AI & machine learning, software development, sales & marketing, customer support, finance & accounting, and more, the Upwork family of companies enables businesses of all sizes to scale, innovate, and transform their work forces for the age of AI and beyond.

Since its founding, Upwork Inc. has facilitated more than $30 billion in total transactions and services as it fulfills its purpose to create opportunity in every era of work. Learn more about the Upwork Marketplace at  and follow us on Linked In , Facebook , Instagram , Tik Tok , and X ; and learn more about Lifted at Go-Lifted and follow on Linked In .

AI Frontiers operates like a funded startup inside Upwork: a small, senior team with a real product in market and a clear mandate to build the AI-native future of work. We're hiring a Principal Technical Product Manager to own one of our most strategically important 0-to-1 bets end-to-end.

This is, first and foremost, a deeply technical role. You'll set the strategy for moving a human-assisted experience toward an increasingly automated one, and the hardest calls in that transition are technical ones: which parts of the workflow genuinely benefit from a model, which are better served by deterministic systems, and how to sequence automation so quality and trust never regress.

Success in this role means customers moving through the experience end-to-end at a high rate, achieving meaningfully faster time-to-value than today's alternatives, and human effort per engagement trending down as automation lands, all without a drop in quality. This is a builder's role: no committee approvals, no waterfall specs, and broad ownership backed by the infrastructure and runway of a public company.

• Own the product end-to-end: set the vision and roadmap, write and maintain the spec, make hard scope calls across a multi-stage, AI-assisted workflow, and translate messy early-stage reality into crisp strategic recommendations for leadership.
• Make the core AI architecture calls, deliberately choosing among retrieval, prompting, fine-tuning, agentic orchestration, and plain deterministic logic based on what each step of the problem actually requires, rather than defaulting to a model simply because one is available.
• Drive the human-to-AI progression by deciding which human steps to automate first, instrumenting the system so each step is cleanly swappable for AI, and advancing automation on schedule without sacrificing quality or unit economics.
• Build an evaluation-first practice around the product, defining how you measure whether an AI step is good enough to ship, designing evals that catch failure modes like hallucination, drift, and silent degradation before customers do, and turning every workflow step into structured data that drives what gets built, automated, or killed next.
• Coordinate a broad cross-functional surface across engineering (frontend, backend, ML, architecture), design, data, and platform partners (payments, security, legal and privacy, search, reliability), keeping a small, high-velocity core team unblocked.
• Run the operational program in partnership with Operations, scaling the human expert function, supply, and playbooks that power the experience today while steadily reducing human effort per engagement.
• Own go-to-market for early cohorts alongside sales and customer engagement, routing the right customers into the funnel, sharpening the value proposition, and protecting the business model as it scales.

• A deep, first-principles understanding of modern AI, including how LLMs and agentic systems actually work under the hood (context and retrieval, tool use, fine-tuning, evaluation, and the real latency, cost, and reliability tradeoffs involved), with the fluency to hold a technical conversation with ML engineers as a peer.
• Sharp judgment about where AI belongs and where it doesn't, including the ability to tell which problems genuinely need a model from those better solved with deterministic code, and an understanding of model failure modes deep enough to design around them rather than be surprised by them.
• A clear-eyed view of why bolt-on AI so often fails while embedded, well-scoped AI works,…