Staff Backend Engineer

Job description

**Location: Remote (North America) or Austin, TX** **Employment Type: Full-time (no contractors)** **Department: Engineering** ### **Why now** 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](https://devin.ai/customers/hamming) [Ranked #1 Eng team](https://www.linkedin.com/posts/andrew-d-churchill_sumanyu-ceo-at-hamming-ai-had-20x-the-output-activity-7354564854029901824-jSDP?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAh-eI0Bl3EbQhXGlnjcZ4TbyeByvoh32hg) [OpenAI Dev Day 100billion token list](https://www.linkedin.com/posts/sumanyusharma_openaidevday-activity-7381718141455859712-OPhA?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAh-eI0Bl3EbQhXGlnjcZ4TbyeByvoh32hg) ## **What you’ll do** - **Own core services** in **TypeScript/Node.js** and **Python** that orchestrate **LiveKit**, **Temporal**, STT/TTS, and LLM tooling for real-time voice agents. - **Scale 1 → N → 100×**: take what works today and harden it for 10K parallel calls with **99.99%** uptime. Turn human playbooks into productized systems. - **Harden pipelines** for ingestion, evaluation, and analytics so telephony events, recordings, and outcomes propagate reliably across services. - **Level-up observability**: deepen **OpenTelemetry/SigNoz** and trace-first practices to shrink mean-time-to-truth in prod. - **Prototype → test → prod**: partner with product to ship new LLM-driven behaviors with clear success metrics, guardrails, and regressions blocked in CI. - **Infrastructure readiness**: CI/CD, environment automation, incident response playbooks—customer conversations stay online. ## **You might be a fit if you** - Have **senior/staff** experience running distributed backends with **real-time/streaming** constraints. - Are fluent in **TypeScript/Node.js** and comfortable jumping into **Python** for ML/audio jobs. - Know **Temporal** (or similar workflow engines), queues, Redis, and **PostgreSQL**. - Have **shipped production LLM apps** and understand prompt/tool design, evals, and guardrail instrumentation. - Operate cloud-native on **AWS** with **Terraform**; k8s doesn’t scare you. - Are a **power user of Cursor/Zed/Devin** and were using code-gen before it was cool. - Have intuition for what current-gen LLMs can/can’t do—and what tomorrow’s models will unlock. - Think independently, **grind with customers**, and do whatever it takes—without dropping the quality bar. - Bonus: built 0→1 **real-time systems** in Telecom/Networking, Autonomous Vehicles, or HFT; founded something; built **AI voice** apps. ## **Interesting problems you’ll touch** - **Voice simulations that feel real**: accents, overlapping speech, crosstalk, background noise, barge-ins. - **Massive concurrency**: **10,000+ parallel calls** with deterministic behavior and graceful degradation. - **Temporal-driven orchestration** for long-running, interruptible call flows. - **Closed-loop reliability**: turn prod failures into auto-generated tests and blocked deploys. - **Trace-everything** culture: make “what happened?” a 30-second question, not a war room. ## **How we work** - **Outcomes over output**: we adjust roadmaps when new data lands. - **Demo early** and document decisions so context moves fast. - **Own incidents**: lead the investigation, write crisp notes, land durable fixes. - **Direct, candid, respectful** communication keeps remote teammates in lockstep with Austin HQ. ## **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 make **AI voice agents reliable at scale**, let’s talk. Send a short note (links to work > resumes) and tell us about something **reliability-critical** you shipped: what broke, what you fixed, and how you knew it worked.