Product Engineer
About the role
Complete QA platform for voice agents. Own reliability and scale across the LLM-enabled platform, shipping precise, outcome-driven improvements to high-availability systems.
Responsibilities
- 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.
Qualifications
- 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.
Skills
Benefits
- Location: San Francisco, CA (on-site) • Employment Type: Full-time (no contractors) • Department: Engineering
About Hamming AI
Automated voice AI agent testing and monitoring
Hamming automates the QA of voice agents. (both pre-deployment testing and post-deployment analytics) Making voice AI agents reliable is hard. A small change in prompts, function call definitions, or model providers can cause large changes in LLM outputs. We have a proven track record of helping enterprises win with AI. Sumanyu (CEO) previously helped Citizen (safety app) grow its users by 4X and grew an AI-powered sales program to 100s of millions in revenue/year at Tesla. Our eng team ranks #1 on https://workweave.ai/
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