Senior AI Infrastructure Engineer — Scale Training & Inference

Fin; Greater London; Hybrid; GBP 90,000 - 150,000; Full time

Job summary

Fin, now part of Salesforce, is hiring a Senior+ AI Infrastructure Engineer to build training and inference systems for large transformer and LLM models. You will join a small, highly technical team working on GPU‑level performance and scalable pipelines.

You’ll collaborate with ML scientists, own parts of the stack from data ingestion to distributed training and deployment, and contribute to hiring and mentoring. Hybrid London-based role with strong growth in AI infra.

Qualifications

• 5+ years of experience in software engineering.
• Degree in Computer Science, Computer Engineering, or equivalent.
• Hands-on experience with model training and/or model inference at scale.
• Experience with low‑level GPU work (CUDA or Triton).
• Production experience in large-scale environments.
• Strong fundamentals, clear communication, and team collaboration.
• Proficiency in Python and ability to learn new stacks quickly.

Responsibilities

• Implement and scale training pipelines for large transformer and LLM models, from data ingestion to distributed training and evaluation.
• Build and optimize inference services with low latency and high reliability, including autoscaling and routing.
• Tune GPU kernels, improve utilization, and identify bottlenecks across training and inference stacks.
• Collaborate with ML scientists to implement advanced 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 Fin's AI platform.

Skills

Model training

Model inference

CUDA

Triton

Python

Go

Distributed training

Education

BSc in CS

Tools

Kubernetes

AWS

Job description

Fin, now part of Salesforce, is hiring a Senior+ AI Infrastructure Engineer to build training and inference systems for large transformer and LLM models. You will join a small, highly technical team working on GPU‑level performance and scalable pipelines.

You’ll collaborate with ML scientists, own parts of the stack from data ingestion to distributed training and deployment, and contribute to hiring and mentoring. Hybrid London-based role with strong growth in AI infra.