AI Product Engineer, Fireworks AI

• You've built a product people actually use — and you cared about how it felt, not just whether it compiled
• You blend strong engineering with product taste and an instinct for what great developer experience looks like
• You'd rather talk to a customer, ship a prototype, and learn than wait for a perfect spec
• You have a knack for making powerful infrastructure feel simple — without dumbing it down
• You move fast with good judgment: you know when to polish and when to ship and iterate
• 3+ years of experience as a software engineer, with meaningful time spent building user-facing products
• Strong full-stack skills — comfortable across frontend (React/TypeScript), backend (Python, Go, or similar), APIs, and databases
• Experience building developer-facing products: APIs, dashboards, consoles, CLIs, or developer tools
• Track record of owning features end-to-end — from problem discovery through deployment and iteration
• Strong product instincts and developer empathy — you notice when something is off in a workflow and you fix it
• Demonstrated ability to operate at high velocity with extreme ownership: you ship fast, make pragmatic trade-offs, and don't wait to be told what to build
• Comfort talking to customers and incorporating feedback directly into what you build

Desirable

• Product engineers at Fireworks build the software we ship to our users
• You'll join a high-autonomy team that turns frontier AI infrastructure into products developers and enterprises love — from the inference API and developer console to fine-tuning workflows, deployment management, billing, and new bets like Fire Pass and agentic tooling
• This is not a "take a ticket and implement" role
• You'll sit close to customers, talk to users directly, and iterate on what you ship based on real feedback — often within days, not quarters
• You'll own features end-to-end: understand the problem (sometimes by joining a customer call), design the solution, build it across the stack, ship it, instrument it, and improve it until it works in production
• Fireworks owns the full stack: training, fine-tuning (SFT and RFT at frontier scale), serverless and dedicated inference, multi-LoRA serving, and agentic orchestration — all on infrastructure we built from scratch
• Product Engineering sits on top of that stack and makes it usable
• The pace of AI moves week to week; the map is being drawn as we go, and product engineers here help draw it
• Ship product and talk to users. Join customer calls, work with design partners, and turn what you learn into product improvements — not slide decks
• Own features end-to-end across the Fireworks platform: developer console, API surfaces, model playground, fine-tuning workflows, deployment management, billing, and new product bets
• Sprint on 0→1 initiatives. Take ambiguous ideas from concept to shipped product in weeks — new workflows, new product surfaces, new ways for developers to build on Fireworks
• Build for deeply technical users where the product is an API as much as a UI — and sweat the details in both
• Work full stack across frontend (React/TypeScript), backend services (Python, Go), APIs, and data layers to deliver polished, production-quality surfaces
• Iterate with data and conviction. Instrument what you ship, watch how developers use it, run experiments, and improve fast
• Partner across the company with Infrastructure, Applied Research, Growth, Data, and Design — but don't wait for perfect alignment to make progress
• Punch through technical walls. When platform primitives aren't there yet, you'll help define what's needed and build around (or through) the gaps
• Sample Projects:
• Launching a new developer workflow end-to-end in a two-week sprint — from customer problem to shipped console + API surface
• Redesigning a core product flow (deployments, fine-tuning, or billing) after sitting in on customer onboarding sessions
• Building and iterating on agentic product surfaces — tool use, orchestration UX, and harness tooling that developers trust
• Running experiments on API/console behavior to reduce time-to-first-successful-request for new developers
• Shipping a new consumption model or product vertical from scratch with a small cross-functional pod