ML Runtime Engineer

ML Runtime Engineer

Full-Time 59400 - 72600 £ / year (est.) No working from home possible
Fractile

At a Glance

  • Tasks: Integrate AI hardware with inference engines and tackle complex ML challenges.
  • Company: Fractile, a pioneering tech company revolutionising AI inference systems.
  • Benefits: Competitive salary, equity options, health benefits, and 25 days holiday.
  • Other info: Diverse and inclusive workplace with exciting growth opportunities.
  • Why this job: Join a dynamic team solving groundbreaking problems in AI technology.
  • Qualifications: Experience in ML inference, strong software engineering skills, and a passion for innovation.

The predicted salary is between 59400 - 72600 £ per year.

Location: Bristol / London

About Fractile

Fractile was founded in 2022 on the bet that, eventually, the world’s most capable AI systems would be limited in their impact by the time taken to produce useful outputs. We bet everything on the logical conclusion: that the only way to truly unlock this latent value, to make speed viable at scale, was to radically re-invent the hardware that we run our frontier AI models on. Ever since, we have been building chips and systems that tackle this problem: how to efficiently generate output at thousands of tokens per second, while handling the complexity and capacity challenges of operating large models at very long contexts.

The workloads that push to the limits of the current frontier are already transformational; it is the technical and economic limits on inference speed that are constraining progress. The defining work of the 21st century will be marked by the engine of inference delivering immense and diffuse chains of intellectual inquiry, in drug discovery, in software engineering, in materials discovery, in any field where progress is driven by deep reasoning and intelligence to resolve complex problems.

About the Software organisation at Fractile

Developer Experience sits within the Software organisation at Fractile, which is responsible for developing a full software stack for our groundbreaking AI inference systems. That's everything from ML compilers, device drivers and systems firmware, application level runtime and ecosystem integrations, ML and compute libraries, great developer tooling and a full portfolio of simulators, through to datacentre scale workload deployment solutions. At Fractile, we know that a fantastic software stack is a critical and central part of any AI inference solution and it sits at the heart of everything we're doing.

About the team & role

The ML Runtime team is responsible for integrating Fractile's AI accelerators with the latest inference frameworks and building the runtime stack that makes them fly. We work on genuinely hard problems — KV cache management, scalable multi-user inference, and the internals of transformer model execution — alongside a collaborative team that values curiosity and rigour equally.

As an ML Runtime Engineer you will integrate Fractile's AI acceleration hardware with leading inference engines including vLLM and SGLang, research and build proof-of-concept KV cache management implementations tailored to our hardware, and work closely with the broader runtime team to design and build a scalable reference inference engine. You will focus primarily on the transformer ML architecture and share your expertise to help shape the direction of our runtime stack.

About you

You have solid experience with ML inference at scale, including multi-user serving, and a deep understanding of paged attention and inference engines such as vLLM. You are familiar with the key components of the ML software ecosystem and bring strong software engineering skills with an instinct for clean, maintainable systems. You care about depth of knowledge and have a genuine interest in the problem space — not just in shipping, but in understanding why things work the way they do.

Key Requirements

  • Solid experience with ML inference at scale, including multi-user serving
  • Deep understanding of paged attention and inference engines such as vLLM
  • Familiarity with key components of the ML software ecosystem
  • Strong software engineering skills and an instinct for clean, maintainable systems

Nice to Have

  • Experience with Rust
  • Having built your own inference engine from scratch
  • A degree in Computer Science or a related field

What We Offer

  • Competitive salary: A competitive salary reflective of your experience and the specialist nature of the role.
  • Equity & Ownership: meaningful equity so everyone shares in the value creation
  • Benefits: Private Medical, Dental and Vision, Contributory Pension, 25 Days holiday plus bank holidays and Life/Critical Illness Insurance.
  • Diverse & fun office: we believe the hardest problems get solved by the broadest range of minds. We are committed to Equal Employment Opportunity through attracting and retaining a diverse team and building an inclusive environment.

Fractile is seeking to increase the clock speed of global progress, one chip at a time. We’ve recently raised $220M from investors including Founders Fund and Accel and our most important work lies ahead. Join us!

Export controls

Our work involves technologies subject to UK, US and other international export control regulations. Certain roles may require additional eligibility checks to ensure compliance with applicable law. We'll be transparent about this throughout the hiring process.

ML Runtime Engineer employer: Fractile

Fractile is an exceptional employer, offering a unique opportunity to be part of a fast-growing AI hardware startup in London. With a supportive and ambitious team culture, employees are empowered to take ownership of their work while enjoying ample opportunities for personal and professional growth. The company's commitment to innovation and compliance in a cutting-edge field ensures that every team member plays a vital role in shaping the future of technology.

Fractile

Contact Details:

Fractile Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land ML Runtime Engineer

Join Local Tech Meetups

Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at Fractile or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!

Contribute to Open Source Projects

Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to Fractile.

Tap into Online Developer Communities

Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like Fractile.

Explore Job Boards Specifically for Tech Roles

Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like Fractile that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!

We think you need these skills to ace ML Runtime Engineer

ML Inference at Scale
Multi-User Serving
Paged Attention
Inference Engines (e.g., vLLM)
Software Engineering Skills
Clean Code Practices
Understanding of ML Software Ecosystem

Some tips for your application 🫡

Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.

Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Fractile.

Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Fractile and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!

Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!

How to prepare for a job interview at Fractile

Brush Up on Your Coding Skills

For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.

Know Your Tools and Frameworks

Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Fractile uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.

Showcase Your Projects

Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.

Prepare for Behavioural Questions

While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.