Staff Backend Engineer

Location: Remote (North America) or Austin, TX Employment Type: Full-time (no contractors) Department: Engineering   WHY NOW Hamming builds three products for voice and chat AI agents: testing/simulation to validate behavior before launch; red-teaming to probe for prompt injection, jailbreaks, PII leakage, and policy violations; and production monitoring/observability to detect failures in live conversations and turn them into regression tests. 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.