About the job
We're hiring a Senior 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.
Responsibilities
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
Qualifications
Minimum
6+ 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
Preferred
No preferred qualifications listed.