AI Systems Research Engineer — Equity & Impact

United States Digital Space LLC; Greater London; On-site; GBP 101,000 - 192,000; Full time

Benefits offered by this job

Competitive Salary

Equity & Ownership

Private healthcare

Visa sponsorship & relocation benefits

London office

Job summary

The Intelligent Systems Company in London seeks a researcher to advance agentic systems at scale. You will design experiments, analyze failure modes, and build evaluation infrastructure that guides what the company builds next across memory, coordination, and model-hardware interaction.

You will publish findings, collaborate across teams, and shape architectures that combine heterogeneous models and silicon.

Qualifications

• Evidence you can run research of your own: design experiments and produce reusable results.
• Hands-on experience with LLMs in agentic settings: multi-step tasks, tool use, long horizons.
• Real experimental discipline: hypotheses, controlled comparisons, ablations and honest uncertainty.
• Strong engineering: you can code and work in a large shared codebase.

Responsibilities

• Design experiments that isolate why agentic systems fail across behaviour, traces and activations.
• Attack core research themes: memory management, agent steering, task decomposition and continual learning.
• Build agent analysis and evaluation infrastructure: observability, behaviour analysis, evaluation harnesses, auditing.
• Publish evaluation results and methods as company assets and public evidence.
• Collaborate across teams to design scalable solutions for diverse models and silicon.

Skills

Open-ended research

LLMs in agentic settings

Experiment design

Engineering code

Communication

Multi-agent systems

Tools

SGLang

vLLM

inference serving frameworks

Job description

The Intelligent Systems Company in London seeks a researcher to advance agentic systems at scale. You will design experiments, analyze failure modes, and build evaluation infrastructure that guides what the company builds next across memory, coordination, and model-hardware interaction.

You will publish findings, collaborate across teams, and shape architectures that combine heterogeneous models and silicon.