Developer Experience / DevEx

As a member of the Developer Experience team you will need to understand the current state of development for inference hardware. You will need to be able to identify what will work well for engineers wanting to identify potential opportunities for performance improvements, while knowing what tooling will fit into an existing LLM engineer’s workflow with the minimal amount of disruption and effort., We’re looking for someone who is self-driven and capable of identifying tooling opportunities, designing those tools, and delivering them in a way which will fit in with an LLM engineer’s existing toolset. You will have worked in this area before, possibly as an FDE or at a company that provides inference services using their own models, and have the ability to develop new tools that are designed to be functional and easy to learn and use., * Experience with inference frameworks such as JAX and/or PyTorch

• Experience of software development in Python, Rust, C/C++, or a similar language
• Experience monitoring and optimising code written in CUDA, ROCm, OpenCL, or similar
• Experience monitoring and optimising LLM deployments with 50+ billion parameters
• Experience working with stakeholders across multiple teams

• 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!

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.