AI Infrastructure Engineer

Fin is the AI Customer Agent company on a mission to help businesses provide perfect customer experiences. We’re looking for Senior+ AI Infrastructure Engineers to build the systems that train and serve Fin's next generation of AI products. You’ll join a small, highly technical team working at the cutting edge of modern AI infrastructure.

Responsibilities

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 Fin's AI platform

Requirements

Track record of working on model training or model inference at scale; low-level GPU coding (e.g. CUDA, Triton); 5+ years of experience in software engineering; degree in Computer Science, Computer Engineering, or related field (or equivalent experience); hands-on experience with model training, model inference at scale, or low-level GPU work (CUDA/Triton kernels); comfortable working in production environments at meaningful scale; clear communication; strong technical fundamentals; deep knowledge of at least one programming language (e.g. Python, Ruby, Java, Go)

Nice to have

Experience at AI native companies that train and/or run inference for their own models; 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

Benefits

Support for health and wellbeing; support for family; time away from work; financial future programs; hybrid working policy