AI Infrastructure Engineer in London

Intercom is the AI Customer Service company on a mission to help businesses provide incredible customer experiences. Our AI agent Fin, the most advanced customer service AI agent on the market, lets businesses deliver always-on, impeccable customer service and ultimately transform their customer experiences for the better. Founded in 2011 and trusted by nearly 30,000 global businesses, Intercom is setting the new standard for customer service.

We’re looking for Senior+ AI Infrastructure Engineers to build the systems that train and serve Intercom’s next generation of AI products. You’ll join a small, highly technical team working at the cutting edge of modern AI infrastructure. The AI Infra team built the training pipelines and runs the inference for custom models like Fin Apex, which outperforms frontier models in customer service tasks.

We’re particularly interested in engineers who have:

• A track record of working on model training or model inference at scale, or on low‑level GPU coding (e.g. CUDA, Triton).

What will I be doing? As a Senior AI Infrastructure Engineer focused on model training and inference, you will:

• 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 our customers, including autoscaling, routing, and fallbacks.
• Work on GPU‑level performance: tuning kernels, improving utilization, and identifying bottlenecks across our 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.

Profile we’re looking for:

• You have 5+ years of experience in software engineering, with a strong track record of shipping high‑quality products or platforms.
• You hold a degree in Computer Science, Computer Engineering, or a related field (or you have equivalent experience with very strong fundamentals).
• You have hands‑on experience with one or more of the following:
• Model training (especially transformers and LLMs).
• Model inference at scale (again, especially transformers and LLMs).
• Low‑level GPU work, such as writing CUDA or Triton kernels.
• Comfortable working in production environments at meaningful scale (traffic, data, or organizational).
• You communicate clearly, can explain complex technical topics to different audiences, and enjoy close collaboration with both engineers and non‑engineers.
• You take pride in strong technical fundamentals, love learning, and are willing to invest in your own development.
• Have deep knowledge of at least one programming language (for example Python, Ruby, Java, Go, etc.).

Bonus skills & attributes:

• 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.

Benefits:

• Competitive salary and equity in a fast-growing start-up.
• We serve lunch every weekday, plus a variety of snack foods and a fully stocked kitchen.
• Regular compensation reviews - we reward great work!
• Unlimited access to Claude Code and best-in-class AI tools; experimentation & building is encouraged & celebrated.
• Pension scheme & match up to 4%.
• Peace of mind with life assurance, as well as comprehensive health and dental insurance for you and your dependents.
• Flexible paid time off policy.
• Paid maternity leave, as well as 6 weeks paternity leave for fathers.
• Cycle-to-Work Scheme with secure bike storage.
• MacBooks are our standard, but we also offer Windows for certain roles when needed.

Fin has a hybrid working policy. We believe that working in person helps us stay connected, collaborate easier and create a great culture while still providing flexibility to work from home. We expect employees to be in the office at least three days per week.

Fin values diversity and is committed to a policy of Equal Employment Opportunity. Fin will not discriminate against an applicant or employee on the basis of race, color, religion, creed, national origin, ancestry, sex, gender, age, physical or mental disability, veteran or military status, genetic information, sexual orientation, gender identity, gender expression, marital status, or any other legally recognized protected basis under federal, state, or local law.