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Product Engineer at Hamming AI. Remote Location: Austin. Location: Remote (North America) or Austin, TX. Employment Type: Full-time (no contractors). Department: Engineering. About Hamming AI. Hamming automates QA for voice AI agents. Everyone is building voice agents. We secure them. In fact, we invented this category. With one click, . thousands of our agents call our customers’ agents. across accents, background noise, and personalities—then we generate . crisp bug reports. and production-grade analytics. Reliability is the moat in voice AI, and that’s our whole job.. We are one of the fastest engineering teams in the world. We prod deploy 4x / day.. I’m looking for someone who can . own reliability and scale. across our LLM-enabled platform, shipping precise, outcome-driven improvements to high-availability systems.. — . Sumanyu (CEO). Previously: grew Citizen 4× and scaled an AI sales program to $100Ms/yr at Tesla.. Devin Case Study. Ranked #1 Eng team. OpenAI Dev Day 100billion token list. What you’ll do. Own product features end-to-end. : spec → prototype → ship → iterate, across frontend and backend.. Work closely with customers:. onboard new accounts, run weekly check-ins, and act as a high-agency partner to drive adoption and outcomes.. Build core customer workflows. for voice-agent QA: test creation, scenario management, evaluation results, analytics, debugging, and triage.. Turn messy, high-dimensional data (calls, transcripts, tool events, traces, eval outputs) into . product experiences that are obvious and actionable.. Partner with customers . to understand their reliability pain, then translate it into shipped product with measurable outcomes.. Tighten the product loop:. instrumentation, funnels, and feedback so we know what’s working and what’s not.. Maintain high engineering velocity while keeping craftsmanship. : clean APIs, strong abstractions, and excellent UI polish.. You might be a fit if you. Have 3+ years building customer-facing products. in a high-velocity environment (startup experience a plus).. Are fluent in TypeScript. and comfortable across the stack (React/Next.js + Node services).. Ship quickly but with discipline:. you write clear code, strong tests where it matters, and avoid accidental complexity.. Have strong product instincts:. you can simplify complex workflows into crisp UX and make good tradeoffs under ambiguity.. Love talking to users,. diagnosing friction, and iterating until a feature feels “done.”. Care about reliability:. you build with observability, failure modes, and data correctness in mind.. Communicate clearly: . written specs, crisp PRs, and decisions that scale across a fast-moving team.. Bonus. Experience building analytics-heavy products . (dashboards, event pipelines, debugging tools).. Familiarity with LLM apps. , evals, tool calling, or prompt/guardrail systems.. Experience with real-time systems,. telecom/voice, or high-concurrency workflows.. Strong UI craft:. interaction design, information architecture, and performance tuning.. Interesting problems you’ll touch. Debugging workflows for voice agents:. call timelines, transcripts, tool calls, traces, and “what changed?” diffs.. Test authoring that scales:. scenario libraries, parameterization, coverage, and regression packs.. Evaluation UX: . turning model-graded / heuristic / human feedback into trustworthy signals and action items.. Analytics that matter:. reliability metrics customers can run their business on (not vanity charts).. Enterprise readiness in-product:. RBAC, audit trails, data retention, and environment/region controls.. Our stack. App. : Next.js, TypeScript, Tailwind. AI. : OpenAI, Anthropic, STT/TTS providers. Realtime/Orchestration. : LiveKit, Pipecat/Daily, Temporal. Infra/DB. : AWS, k8s, PostgreSQL, Redis, Terraform. Observability. : OpenTelemetry, SigNoz. Apply. If you want to build . the product layer for reliable Voice AI. , let’s talk.. Send a short note (links to work > resumes) to . [email protected]. and tell us about a product you shipped end-to-end: what you built, where it was painful, what tradeoffs you made, and how you knew it worked.