At a Glance
- Tasks: Build and optimise AI training pipelines and inference services for cutting-edge models.
- Company: Join Fin, a fast-growing AI company transforming customer support globally.
- Benefits: Competitive salary, equity, flexible time off, and comprehensive health benefits.
- Other info: Collaborative environment with opportunities for mentorship and career growth.
- Why this job: Work at the forefront of AI technology and make a real impact on customer experiences.
- Qualifications: 5+ years in software engineering with experience in model training or GPU coding.
The predicted salary is between 70000 - 90000 £ per year.
About Fin: 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. 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.
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. 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.
- Raise the bar for technical standards, reliability, and operational excellence across Fin's AI platform.
Profile we’re looking for: We’re looking to hire Senior+ AI Infrastructure Engineers. You’re likely a great fit if:
- You have 5+ years of experience in software engineering, with a strong track record of shipping high‑quality products or platforms.
- You hold a degree in Computer Science, Computer Engineering, or a related field (or you have equivalent experience with very strong fundamentals).
- You have hands‑on experience with one or more of the following:
- Model training (especially transformers and LLMs).
- Model inference at scale (again, especially transformers and LLMs).
- Low‑level GPU work, such as writing CUDA or Triton kernels.
- Comfortable working in production environments at meaningful scale (traffic, data, or organizational).
- You communicate clearly, can explain complex technical topics to different audiences, and enjoy close collaboration with both engineers and non‑engineers.
- You take pride in strong technical fundamentals, love learning, and are willing to invest in your own development.
- Have deep knowledge of at least one programming language (for example Python, Ruby, Java, Go, etc.). Specific language experience is less important than your ability to write clean, reliable code and learn new stacks quickly.
- Experience at AI native companies that train and/or run inference for their own models (e.g. modern AI labs or AI‑native product companies).
- Experience running training or inference workloads on Kubernetes.
- Experience with AWS or other major cloud providers.
- Production experience with Python in ML or infrastructure contexts.
- Demonstrated passion for technology through personal projects, open source, meetups, or publishing content about your work and learnings.
Benefits: We are a well treated bunch, with awesome benefits! If there’s something important to you that’s not on this list, talk to us!
- Competitive salary and equity in a fast-growing start-up.
- We serve lunch every weekday, plus a variety of snack foods and a fully stocked kitchen.
- Regular compensation reviews - we reward great work!
- Unlimited access to Claude Code and best‑in‑class AI tools; experimentation & building is encouraged & celebrated.
- Pension scheme & match up to 4%.
- Peace of mind with life assurance, as well as comprehensive health and dental insurance for you and your dependents.
- Flexible paid time off policy.
- Paid maternity leave, as well as 6 weeks paternity leave for fathers, to let you spend valuable time with your loved ones.
- If you’re cycling, we’ve got you covered on the Cycle‑to‑Work Scheme. With secure bike storage too.
- MacBooks are our standard, but we also offer Windows for certain roles when needed.
AI Infrastructure Engineer employer: Fin
Fin is an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration among its highly skilled team. With competitive salaries, equity options, and a range of benefits including flexible paid time off and comprehensive health insurance, employees are well-supported in both their professional and personal lives. Located in a fast-paced environment, Fin encourages continuous learning and growth, making it an ideal place for those looking to advance their careers in AI infrastructure.
StudySmarter Expert Advice🤫
We think this is how you could land AI Infrastructure Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and tech space, especially those at Fin or similar companies. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! If you've got projects or contributions to open source, make sure to highlight them. A portfolio speaks volumes about your capabilities and passion.
✨Tip Number 3
Prepare for technical interviews by brushing up on your coding skills and understanding of AI infrastructure. Practice common problems and be ready to discuss your past experiences in detail.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in being part of our team at Fin.
We think you need these skills to ace AI Infrastructure Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the AI Infrastructure Engineer role. Highlight your experience with model training, inference, and any GPU coding you've done. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about AI and how your background makes you a perfect fit for our team. Keep it engaging and personal – we love to see your personality come through.
Showcase Your Projects:If you've worked on any relevant projects, whether personal or professional, make sure to mention them. We’re keen to see your hands-on experience, especially with transformers, LLMs, or any cool AI stuff you've built!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you're serious about joining our awesome team at Fin!
How to prepare for a job interview at Fin
✨Know Your AI Stuff
Make sure you brush up on your knowledge of model training and inference, especially with transformers and LLMs. Be ready to discuss your hands-on experience with GPU coding, like CUDA or Triton, as this will be crucial for the role.
✨Showcase Your Problem-Solving Skills
Prepare to share specific examples of how you've tackled performance issues or bottlenecks in previous projects. Highlight your ability to optimise systems for low-latency and high-reliability experiences, as this aligns perfectly with what the company is looking for.
✨Communicate Clearly
Practice explaining complex technical concepts in simple terms. You’ll need to demonstrate that you can collaborate effectively with both engineers and non-engineers, so being able to communicate clearly is key.
✨Be Ready to Discuss Team Dynamics
Since you'll be playing a role in mentoring and developing other engineers, think about your past experiences in team settings. Be prepared to talk about how you've contributed to team success and raised technical standards in your previous roles.