Machine Learning Engineer

ML Model Serving Engineer

Want to build the layer that actually makes AI usable in real time?

You’ll join a team focused on inference, where performance is the product. This is about delivering low-latency, high-throughput systems across LLMs, speech, and vision models running in production, not offline experiments.

They’re building real-time AI systems that need to respond instantly, reliably, and at scale. That means solving hard problems around batching, GPU efficiency, memory constraints, and system-level bottlenecks that most teams never fully crack.

You’ll sit at the core of the platform, working across model serving, infrastructure, and performance optimisation. A big part of the role is pushing current tooling beyond its limits, extending frameworks, profiling bottlenecks, and designing systems that hold up under real-world load.

This is not about training models. It’s about making them fast, efficient, and production-ready.

What you’ll work on:

• Building high-performance serving systems for LLM, speech, and vision models
• Scaling inference to production workloads with strict latency requirements
• Optimising GPU utilisation and execution efficiency
• Implementing techniques like continuous batching, KV cache optimisation, speculative decoding, and prefill/decode separation
• Improving frameworks such as vLLM, TensorRT-LLM, Triton, and SGLang
• Profiling and debugging performance across GPU, memory, and system layers

What you’ll bring:

• Strong experience with ML inference or model serving systems
• Deep understanding of latency and throughput optimisation in production
• Solid Python and PyTorch skills, plus a systems or performance engineering mindset
• Familiarity with distributed systems and production infrastructure

Exposure to CUDA, GPU profiling tools, or systems like Kubernetes and Ray is useful, but the key is knowing how to make models run efficiently at scale.

You’ll join a highly technical team with experience across major AI labs and big tech. The environment is pragmatic, focused on solving real performance problems rather than abstract research.

There’s real ownership here. You’ll help define how next-generation AI systems are served.

Package:

$220,000 – $320,000 base + equity

San Francisco, onsite 3 days per week

If you’re interested in working on the part of AI that actually determines whether it works in the real world, this is worth exploring.

All applicants will receive a response.