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. Fin is an AI company that builds from the GPU all the way up to a user agent that resolves millions of customer service queries a month.

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 by 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, or on 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; Comfortable working in production environments at meaningful scale; Clear communication and ability to explain complex technical topics; Strong technical fundamentals and willingness to learn; Deep knowledge of at least one programming language

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 about your work and learnings

Benefits

Hybrid working policy; Benefits and programs designed to support health and wellbeing, family, time away from work, and financial future