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Fin, an AI Customer Agent company, is seeking Senior AI Infrastructure Engineers to build the systems that train and serve the next generation of AI products. Fin is a full-stack AI company that builds from GPU infrastructure up to user-facing AI agents resolving millions of customer service queries monthly.
You'll join a small, highly technical team working at the cutting edge of modern AI infrastructure. The AI Infrastructure team built the training pipelines and runs inference for custom models like Fin Apex, which outperforms frontier models in customer service tasks.
Key responsibilities include:
- Implementing and scaling training pipelines for large transformer and LLM models, from data ingestion and preprocessing through distributed training and evaluation
- Building and optimizing inference services delivering low-latency, high-reliability experiences, including autoscaling, routing, and fallbacks
- Working on GPU-level performance: tuning kernels, improving utilization, and identifying bottlenecks across training and inference stacks
- Collaborating closely with ML scientists to implement cutting-edge training and inference methods and bring them to production
- Hiring, mentoring, and developing other engineers on the team
- Raising technical standards, reliability, and operational excellence across Fin's AI platform
Ideal candidates have 5+ years of software engineering experience with a strong track record of shipping high-quality products or platforms. You should have hands-on experience with model training (especially transformers/LLMs), model inference at scale, or low-level GPU work (CUDA, Triton). Comfort working in production environments at meaningful scale, clear communication skills, strong technical fundamentals, and deep knowledge of at least one programming language are essential. A degree in Computer Science, Computer Engineering, or related field (or equivalent experience with strong fundamentals) is preferred.
Bonus experience includes working at AI-native companies that train and/or run inference for their own models, or running training/inference workloads on Kubernetes.
