Product Engineer

Location:
Remote (North America) or Austin, TX

Employment Type:
Full-time (no contractors)

Department:
Engineering

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 $100

Ms/yr at Tesla.

++Devin Case Study++

++Ranked #1 Eng team++

++OpenAI Dev Day 100billion token list++

• 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.

• 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.

• 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.

• App
  :
  Next.js, Type Script, Tailwind
• AI
  :
  OpenAI, Anthropic, STT/TTS providers
• Realtime/Orchestration
  :
  Live Kit, Pipecat/Daily, Temporal
• Infra/DB
  : AWS, k8s, Postgre
  
  SQL, Redis, Terraform
• Observability
  :
  Open Telemetry, Sig Noz

If you want to build the product layer for reliable Voice AI
, let’s talk.

Send a short note (links to work > resumes) to careers 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.