Staff Backend Engineer
hamming ai · Austin ·
- Work mode
- Hybrid
- Seniority
- Staff
- Employment
- Full time
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- Backend
hamming ai · Austin ·
Location: Remote (North America) or Austin, TX
Employment Type: Full-time (no contractors)
Department: Engineering
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
Ranked #1 Eng team
OpenAI Dev Day 100billion token list
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.
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.
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.
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.
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
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.