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Gradium provides developers and companies with real-time voice APIs for transcription, synthesis and conversational agents. Its technology enables natural, low-latency voice interactions.

Information about Gradium
Sector
Software and internet
Model
B2B
Company type
Venture-backed
Stage
Startup early-stage
Headquarters
Paris
Official domain
gradium.ai
Founded
2025 · 1 year
Published headcount
26 employees
Total funding published
100 M$Source ↗
Latest funding round
Seed extension · 2026-07-09
Amount announced
30 000 000 $
Open jobs
8

Investors mentioned

Job details

The full description published by Gradium.

View original job listing ↗

Description de l’offre

About Gradium Gradium is a frontier voice AI company on a mission to redefine how humans interact with machines . Voice remains the most human, highest-stakes channel, but also the most broken. Long wait times, rigid IVRs, low automation, and poor handoffs create frustration on both sides of the line. Gradium is rebuilding voice from the ground up with proprietary models: real-time understanding, autonomous resolution, and seamless escalation when humans matter most.

We're a team of world-class talent, with initial traction and a clear belief: voice will be the next major frontier of applied AI. We recently raised a $100m seed round and are backed by top-tier investors. Our goal is not incremental improvement: it's to make AI-powered voice interactions feel reliable, scalable, and economically transformative. Expect early-stage reality: high autonomy, fast decisions, unreasonable ambition, few handoffs, and very little process unless it earns its place.

The Role

We're looking for a software engineer to build the core backend and infrastructure that turns Gradium's models into a product customers trust. You'll design the services behind real-time audio, keep latency low for users around the world, and make sure production stays up when it matters.

You'll work at the heart of the company, partnering closely with the founders and the research team to deploy and serve models efficiently at scale. This role exists because our edge is not just model quality but how reliably we deliver it, and that is an engineering problem that needs an owner.

What You'll Do

- Build the backend behind real-time audio: Design and build scalable backend services for real-time audio processing, and keep API latency low for users around the world.

- Make production bulletproof: Implement robust monitoring, logging, and alerting, and ensure high availability and reliability of the systems customers depend on.

- Serve models efficiently: Work with the research team to deploy and serve models in production without wasting compute.

- Own reliability at scale: Keep systems fast and dependable as traffic grows, and reason clearly about the trade-offs that come with distributed systems.

Who You Are

  • Founder mindset: You act with urgency, take full ownership, and don't wait for permission or perfect information. You are comfortable making high-stakes decisions in ambiguous environments and see the founding team as partners, not hierarchy.

- Production-obsessed engineer: You care about how systems behave under real load, not just in a demo. You measure everything, chase latency and reliability relentlessly, and take pride in things that don't break.

- Backend and distributed systems expertise: You have 5+ years of backend engineering with strong Python or Rust, hands-on experience with distributed systems, and proficiency with a major cloud platform (AWS, GCP, or Azure). You have a BS in Computer Science or equivalent practical experience.

- AI-fluent operator: You use AI-powered tools in your daily work and can always articulate why. You're relentless about pushing what's possible and reinventing how you and your team operate.

Bonus points for :

  • real-time audio or video processing systems
  • WebRTC and streaming protocols
  • Kubernetes and container orchestration
  • ML model serving infrastructure.

Still interested?

We'd love to hear about you!

Requirements

  • Distributed systems expertise
  • Major cloud platform proficiency
  • BS in Computer Science or equivalent practical experience
  • Real-time audio or video processing systems
  • ML model serving infrastructure

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