Fin is the AI Customer Agent company on a mission to help businesses provide perfect customer experiences
Our AI Agent Fin is the highest-performing AI Customer Agent on the market today, enabling businesses to deliver impeccable, always-on customer support across the customer journey – from service, to sales, to ecommerce. Powered by our own AI models, Fin resolves complex customer issues end-to-end across every channel, with minimal set-up and integration. Fin can also be combined with our natively integrated Intercom help desk for one single system that is designed to meet the needs of modern day support teams
Founded in 2011, Fin became one of the fastest growing companies and remains one of the largest private software companies in the world with nearly 30,000 global businesses using our products to transform their customer support. Driven by our core values, we push boundaries, build with speed and intensity, and relentlessly deliver incredible value to our customers
What's the opportunity? 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
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, and is the foundation of the AI Group's full stack approach to AI
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). Experience with one is great, multiple is even better
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.<