Requirements

A track record of working on model training or model inference at scale, or on low‑level GPU coding (e.g. CUDA, Triton). Experience with one is great, multiple is even better.

What You'll Be Doing

Implement and scale training pipelines for large transformer and LLM models, from data ingestion and preprocessing through distributed training and evaluation.
Build and optimize inference services that deliver low‑latency, high‑reliability experiences for customers, including autoscaling, routing, and fallbacks.
Work on GPU‑level performance: tuning kernels, improving utilization, and identifying bottlenecks across the training and inference stack.
Collaborate closely with ML scientists to implement cutting edge training and inference methods and bring them to production.
Play an active role in hiring, mentoring, and developing other engineers on the team.
Raise the bar for technical standards, reliability, and operational excellence across Intercom’s AI platform.

Experience at AI native companies that train and/or run inference for their own models (e.g. modern AI labs or AI‑native product companies).
Experience running training or inference workloads on Kubernetes.
Experience with AWS or other major cloud providers.
Production experience with Python in ML or infrastructure contexts.
Demonstrated passion for technology through personal projects, open source, meetups, or publishing content about your work and learnings.