ВеликобританияMember of Technical Staff (EMEA)
2 мес. назад
ВеликобританияLeadHybrid
pythonllmsoftware engineering
Develop software for one of the largest AI inference platforms, focusing on building product-facing capabilities and tooling for large language models.
О компании
- is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI.
- Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving.
- Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally.
- Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results.
- Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation.
- AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.
Обязанности
- As a Member of Technical Staff on the EMEA field team, you build on top of one of the largest inference platforms in the world. The work spans the stack: capabilities on top of inference and serving, tooling around fine-tuning and post-training of LLMs, and the product our region’s customers need that the core platform team will not take on. You are first and foremost an excellent software engineer; that is what we are testing for. You prefer building product to being customer-facing, and that is exactly right for this role, though you are welcome to join customer work when it interests you.
- Treat this as a flexible role: a bucket for exceptional builders at different levels of experience rather than a fixed job spec. There is no specific seniority requirement; anywhere from 3 to 10 years of experience can fit.
- What we value most is first principles thinking: reasoning about unfamiliar problems from the ground up rather than from playbooks. You will be evaluated on this directly in the interview process, and strength here can outweigh gaps elsewhere in your background.
- Build: Design and ship product capabilities end to end, from ambiguous need to running in production.
- Build on Inference: Create capabilities on top of the serving stack: routing, model controls, observability, and whatever a deployment turns out to need.
- Build on Training: Ship tooling around fine-tuning and post-training of LLMs, from data pipelines to evaluation, working alongside our applied ML engineers.
- Own Regional Capability: Turn EMEA-specific requirements into product, working with core platform where the line is unclear.
- Engineering Quality: Set the engineering bar for the regional team; your code is the reference.
- Agentic Development: Use coding agents and AI tooling as a core part of how you build, with judgment about when to trust the output.
- Optional Customer Work: Join deployments or customer conversations when useful or interesting; never required.
Требования
- First principles and critical thinking: the ability to break down novel problems and reason to a solution without a reference implementation. This is the primary interview signal and can outweigh other qualifications.
- Excellent software engineering: depth in backend systems and infrastructure, and a track record of shipping production systems you owned.
- Strong coding skills in Python and at least one systems language.
- Fluent in AI-assisted and agentic engineering: uses coding agents and AI tooling as a core part of how you build, and knows when to trust the output and when not to.
- Genuine interest in inference and AI infrastructure.