Machine Learning Engineer, Global Public Sector

The shape of it

In the posting’s own words

Your role is to move beyond off-the-shelf implementations. You will lead the research into Agent Design, Reliability, and AI Safety, developing novel system architectures that power high-stakes government applications. You will be the bridge between a research paper and a production-ready system that functions at scale.

What it asks for · 5

• Engineering Rigour: Exceptional proficiency in Python and experience building agentic harnesses or AI infrastructure. You write production-ready code that is modular, scalable, and reliable.
• Applied Research Mindset: A track record of taking theoretical AI concepts and turning them into functional prototypes or products. You know how to read a paper and determine if its methods are actually viable for a production system.
• Evaluation Expertise: Experience in LLM benchmarking, red-teaming, or building evaluations that go beyond standard academic datasets.
• Advanced Degree: A Master’s or PhD in Computer Science, Mathematics, or a related field (with a focus on ML) is preferred, but we value demonstrated impact and engineering excellence.
• Agentic Systems Expert: Deep experience in building multi-agent systems, including chain-of-thought optimisation and tool-calling reliability.

Also a plus

• Agentic Systems Expert: Deep experience in building multi-agent systems, including chain-of-thought optimisation and tool-calling reliability.
• Sovereign AI Experience: Experience working with highly regulated data environments, on-premise deployments, or sensitive government use cases.
• Inference Optimisation: Knowledge of how to optimise model performance for environments with limited GPU capacity or specific latency requirements.
• Zero-to-One Mindset: You are comfortable navigating ambiguity and enjoy defining research directions from scratch to solve a specific product or mission need.

What the job covers

• Architect Agentic Systems: Design and build agent architectures, the harnesses, tool-use protocols, and logic flows that allow LLMs to function as reliable, autonomous agents in complex workflows.
• Drive Reliability & Safety: Research and implement robust evaluation frameworks. This includes red-teaming for sovereign AI requirements and developing strategies to mitigate hallucinations in regulated data environments.
• Synthesise Deep Research: Build agents capable of autonomous information synthesis and long-horizon reasoning, enabling users to analyse massive datasets and extract actionable insights.
• Optimize for Niche Domains: Evaluate and adapt models for specialised use cases, such as LLM reasoning for low-resource languages, complex OCR tasks, or working in GPU-constrained environments
• Build Evaluation Frontiers: Create new, automated benchmarks that define what success looks like for AI in the public sector, ensuring our systems meet the highest standards of accuracy and sovereignty.
• Consult as a Technical Authority: Act as a subject matter expert for public sector leaders, advising on the practical limits, safety requirements, and performance trade-offs of emerging AI technologies.

Degree language

• Advanced Degree: A Master’s or PhD in Computer Science, Mathematics, or a related field (with a focus on ML) is preferred, but we value demonstrated impact and engineering excellence.

Tools and skills named

Words the posting leans on

• research10×
• systems10×
• evaluation7×
• agent5×
• experience5×
• impact4×
• llm4×
• model4×
• reliable4×
• visa4×
• agentic systems3×
• build3×
• complex3×
• design3×
• developing3×
• engineering3×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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