Senior / Staff Backend Engineer
Hamming, Austin, TX, United States
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.
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.
#J-18808-Ljbffr
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.
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.
#J-18808-Ljbffr