Research Engineer, Model Inference & Serving - London

Research Engineer, Model Inference & Serving

About H: H exists to push the boundaries of superintelligence with agentic AI. By automating complex, multi-step tasks typically performed by humans, AI agents will help unlock full human potential. H is hiring the world's best AI talent, seeking those who are dedicated as much to building safely and responsibly as to advancing disruptive agentic capabilities. We promote a mindset of openness, learning, and collaboration, where everyone has something to contribute.

About the Team: The Inference team builds and operates the systems that serve H's foundational models in production. We focus on multimodal inference and serving for Computer Use Agents, optimizing across both the inference engine layer (e.g., vLLM, SGLang) and the model serving layer (e.g., disaggregated inference, intelligent routing). Agentic inference brings constraints around context length, multimodality, and tool calls, which we address by co-designing with the Models team on training-time choices and with the agent teams on how models are deployed. We operate at the intersection of research and production, translating cutting-edge inference techniques into the systems that power H's next generation of agents. We are looking for strong engineers excited about inference to join the team and help shape the systems behind superintelligent AI.

Key Responsibilities:

Build and operate the inference stack that serves H's multimodal agentic models

Improve latency, throughput, and cost of model serving across the stack

Research and implement inference techniques tailored to agent workloads

Co-design with the Models team on training-time decisions that affect inference

Collaborate with cross-functional teams to integrate inference into agentic AI products

Evaluate inference, serving, and hardware platforms, and communicate findings to stakeholders

Stay current with advancements in inference, model serving, and accelerator technology

Requirements:

Technical skills:

Strong software engineering track record

Proficient in Python and at least one systems language (Rust, C++, or Go)

Hands-on experience with deep learning frameworks (PyTorch, JAX), preferably in an industry setting

Solid distributed systems fundamentals

Experience working in a modern cloud environment and with production ML infrastructure (Kubernetes, etc.)

Working knowledge of modern ML, including transformers and multimodal architectures

Research skills:

Research engagement: an advanced degree with research output, or publications at top-tier AI or systems venues (e.g., NeurIPS, ICML, MLSys, OSDI), research internships, or substantive open-source contributions

Soft skills:

Excellent communication and presentation skills

Strong collaboration and teamwork skills