AI Infrastructure Engineer
Salary not listed • Dublin, Ireland • Full-time
Posted 6mo ago
This job has been open for more than 60 days. It may no longer be actively hiring, so check with the company before putting a lot into an application.
Job Location
Dublin, Ireland
Also open in London, England
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
AI Infrastructure Engineer
About the job
Fin, now part of Salesforce, is seeking Senior+ AI Infrastructure Engineers to build the systems that train and serve their 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, responsible for training pipelines and inference for custom models. 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.
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 customers, including autoscaling, routing, and fallbacks.
- Work on GPU-level performance: tuning kernels, improving utilization, and identifying bottlenecks across the 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 with strong fundamentals).
- Hands-on experience with model training (especially transformers and LLMs).
- Hands-on experience with model inference at scale (especially transformers and LLMs).
- Hands-on experience with low-level GPU work, such as writing CUDA or Triton kernels.
- Comfortable working in production environments at meaningful scale (traffic, data, or organizational).
- Clear communication skills, ability to explain complex technical topics to different audiences, and enjoyment of close collaboration.
- Strong technical fundamentals, love of learning, and willingness to invest in personal development.
- Deep knowledge of at least one programming language (e.g., Python, Ruby, Java, Go).
Benefits
- Support for health and wellbeing
- Support for family
- Support for time away from work
- Support for financial future
- Benefits and programs vary by location