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

Ranked #1 Eng team

OpenAI Dev Day 100billion token list

 

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.