Tech Lead
About the role
Location: Remote (North America) or Austin, TX
Employment Type: Full-time (no contractors)
Department: Engineering
About Hamming AI
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.
OpenAI Dev Day 100billion token list
Why this role matters
We’re moving fast, shipping constantly, and need a tech lead who can guide the team without slowing it down.
This isn’t a people-manager role hidden behind meetings. You’ll lead a team of five engineers, make architecture calls, mentor others, and still ship meaningful code every week. You’ll be in the middle of everything - from scaling our evaluation engine to making sure customers love the product.
What you’ll own
Technical direction across the stack - backend, frontend, and infra. You’ll decide what’s “good enough” today and what deserves to be rebuilt.
Team leadership: unblock engineers, set clear priorities, run lightweight design reviews, and keep the team aligned without red tape.
System reliability: make sure our platform stays fast, observable, and stable while traffic grows 100x.
Hands-on delivery: contribute to key projects weekly — from new product experiments to hardening real-time systems.
Cross-functional glue: keep product and operations connected so decisions flow and context isn’t lost.
What you’ll do day to day
Write code (TypeScript, Python, or both) alongside your team.
Review architecture and PRs - guide decisions without micromanaging.
Help define sprint priorities and keep roadmaps tight and realistic.
Pair with engineers when things break - not just to fix them, but to make sure they don’t break again.
Run post-mortems that actually lead to better systems, not longer docs.
Evaluate new tech (LLMs, infra tools, observability stacks) and decide what’s worth bringing in.
You might be a fit if you
Have 6 - 10 years leading 3 - 6 engineer pods in high-availability, high-frequency deploy shops.
Built and operated realtime/distributed systems (workflow engines, WebRTC/telephony, large fan-out queues).
Still enjoy shipping code and debugging hairy issues yourself.
Have strong fundamentals across backend, infra, and architecture, and can also talk product tradeoffs fluently.
Can move comfortably between TypeScript/Node.js, Python, and cloud infra (AWS, Terraform, Kubernetes).
Understand how to design LLM-powered systems that are testable, measurable, and resilient.
Communicate clearly - direct, concise, and respectful. You default to writing things down.
Care about outcomes more than process.
Bonus points if you’ve:
Built AI products (especially voice or real-time systems).
Operated at an early-stage startup before - and liked it.
Have an instinct for how to turn chaos into repeatable systems.
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 lead a small, senior team that’s building the backbone of reliable voice AI, we’d love to talk.
Send a short note (no formal cover letter needed) to [email protected] — tell us about something you built that scaled fast and stayed reliable.