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Fin, an AI customer service agent company, is hiring Senior AI Infrastructure Engineers to build the systems that train and serve its next-generation AI products. The role focuses on model training pipelines, inference services, and GPU-level optimization for large transformer and LLM models.
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 for low-latency, high-reliability customer experiences, including autoscaling, routing, and fallback mechanisms
- GPU-level performance tuning: optimizing kernels, improving utilization, and identifying bottlenecks across training and inference stacks
- Collaborating 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. Required expertise in at least one of: model training (especially transformers/LLMs), model inference at scale, or low-level GPU work (CUDA, Triton). Comfortable working in production environments at meaningful scale. Strong communication skills, technical fundamentals, and deep knowledge of at least one programming language (Python, Ruby, Java, Go, etc.). Bonus: experience at AI-native companies training/running inference for their own models, or experience with Kubernetes for ML workloads.
