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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 rapidly growing company (nearly 30,000 global businesses use their products) that builds AI infrastructure from GPU-level optimization all the way up to production 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 and forms the foundation of Fin's full-stack AI approach.
Key responsibilities include:
- Implementing and scaling training pipelines for large transformer and LLM models, covering data ingestion, preprocessing, distributed training, and evaluation
- Building and optimizing inference services delivering low-latency, high-reliability experiences with autoscaling, routing, and fallback mechanisms
- GPU-level performance work: tuning kernels, improving utilization, and identifying bottlenecks across training and inference stacks
- Close collaboration with ML scientists to implement cutting-edge training and inference methods in 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, strong technical fundamentals, and deep knowledge of at least one programming language are essential. Bonus experience includes working at AI-native companies that train/run inference for their own models or running workloads on Kubernetes.
