Machine Learning Engineer, Global Public Sector
Core
Designing and building reliable, autonomous agentic systems and long-horizon reasoning frameworks for high-stakes government applications and public policy.
Role type
Senior Applied ML Research Engineer (Agentic Systems & AI Safety)
Builds
Production-ready agentic harnesses, evaluation protocols, and optimized inference systems for sovereign AI
Domain
Public Sector / Government / Sovereign AI / LLMs
Deliverable
production ML models
Required skills
Python, agentic system architecture, LLM benchmarking, red-teaming, model optimization, RAG, context engineering, GPU-constrained environment adaptation
Preferred skills
multi-agent system design, chain-of-thought optimisation, tool-calling reliability, low-resource language handling, complex OCR tasks
Technologies
Python, LLMs, RAG, GPU environments
Responsibilities
Design and build agent architectures for autonomous workflows; Develop rigorous evaluation frameworks and red-teaming strategies; Synthesize deep research for autonomous information extraction; Optimize models for niche domains like low-resource languages or GPU constraints; Create automated benchmarks for public sector AI; Advise public sector leaders on AI safety and performance trade-offs
Seniority
Senior, hands-on IC with research leadership