Staff Software Engineer, ML Infrastructure

Decagon is the leading conversational AI platform empowering every brand to deliver concierge customer experiences.

Our technology enables industry-defining enterprises like Avis Budget Group, Block’s Cash App and Square, Chime, Oura Health, and Hunter Douglas to deploy AI agents that power personalized, deeply satisfying interactions across voice, chat, email, SMS, and every other channel.

We’re building a future where customer experiences are being redefined from support tickets and hold music to faster resolutions, richer conversations, and deeper relationships. We’re proud to be backed by world-class investors who share that vision, including a16z, Accel, Bain Capital Ventures, Coatue, and Index Ventures, along with many others.

We’re an in-office company, driven by a shared commitment to excellence and velocity. Our values — Just Get It Done, Invent What Customers Want, Winner’s Mindset, and The Polymath Principle — shape how we work and grow as a team.

The ML Infrastructure team builds the systems that power every stage of Decagon's model lifecycle. We own the platforms for model training, the infrastructure for model evaluation and experimentation, and the routing layer that manages inference across multiple providers.

We work at the intersection of research and production: translating cutting-edge ML models into reliable, scalable systems that run in customer environments. We collaborate closely with Research, Infrastructure, and Product teams to ensure models train efficiently, serve reliably, and deliver exceptional user experiences.

The team values technical rigor, pragmatic decision-making, and building systems that others love to use.

We're hiring a Staff ML Infrastructure Engineer to own the platforms powering Decagon's model training and inference. You'll build distributed training systems, design inference architecture across multiple providers, and create the frameworks that let our Research and Product teams ship faster.

This role is for someone who thrives on technical depth, can lead multi-quarter initiatives, and wants to shape the long-term architecture of our ML stack.

• Design and build distributed training platforms for LLM and multimodal fine-tuning and post-training at scale

• Integrate state-of-the-art training algorithms into production pipelines

• Own inference architecture and multi-provider routing, including failover and optimization

• Lead initiatives to improve latency and cost efficiency across the training and serving stack

• Build evaluation and experimentation infrastructure that enables rapid, reliable iteration

• Drive technical direction, mentor engineers, and establish best practices for ML infrastructure

• 10+ years building ML infrastructure or production systems at scale

• Deep experience with distributed training: multi-node GPU clusters, fault tolerance, and optimization

• Strong understanding of LLM inference: latency optimization, provider tradeoffs, and serving architecture

• Proven track record leading complex, multi-quarter technical projects