AI Infrastructure Engineer

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.

Nice to Haves

• 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.

Perks and Benefits

• Competitive salary and equity in a fast-growing start-up
• Lunch served every weekday, snack foods, and a fully stocked kitchen
• Regular compensation reviews
• Unlimited access to Claude Code and best-in-class AI tools
• Pension scheme and match up to 4%
• Comprehensive health and dental insurance, life assurance
• Flexible paid time off policy
• Paid maternity leave, 6 weeks paternity leave
• Cycle-to-Work Scheme with secure bike storage
• Choice of MacBooks or Windows for certain roles