AI Infrastructure Engineer

We're looking for Senior+ AI Infrastructure Engineers to build the systems that train and serve Fin's next generation of AI products.

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

5+ years of experience in software engineering with a strong track record of shipping high-quality products or platforms; Degree in Computer Science, Computer Engineering, or a related field (or equivalent experience); Hands-on experience with model training (especially transformers and LLMs); Model inference at scale (especially transformers and LLMs); Low-level GPU work such as writing CUDA or Triton kernels; 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 (e.g., Python, Ruby, Java, Go); Track record of working on model training or model inference at scale, or low-level GPU coding (CUDA, Triton)

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

Health and wellbeing support; Family support; Time away from work; Financial future support