Machine Learning Engineer, Global Public Sector

Scale AI Scale AI · Data AI · London, United Kingdom · GPS Engineering

Scale AI is hiring ML Research Engineers to bridge the gap between frontier research and real-world impact for global governments. The role involves leading research into Agent design, Deep Research, and AI Safety/reliability, developing novel methodologies for public sector applications and setting new standards across the organization. Responsibilities include pioneering novel architectures, leading AI safety initiatives, driving deep research capabilities, publishing, consulting, and building evaluation frontiers.

What you'd actually do

  1. Pioneer Novel Architectures: Design and train state-of-the-art models and agents, moving beyond “off-the-shelf” solutions to create custom architectures for complex public sector reasoning tasks.
  2. Lead AI Safety Initiatives: Research and implement robust safety frameworks, including red teaming, alignment (RLHF/DPO), and bias mitigation strategies essential for sovereign AI.
  3. Drive Deep Research Capabilities: Develop agents capable of long-horizon reasoning and autonomous information synthesis to solve complex problems for national security and public policy.
  4. Publish and Contribute: Represent Scale in the broader research community by publishing high-impact papers and contributing to open-source breakthroughs.
  5. Consult as a Subject Matter Expert: Act as a technical authority for public sector leaders, advising on the theoretical limits and safety requirements of emerging AI.

Skills

Required

  • Python
  • PyTorch/JAX
  • Deep Learning
  • Agent design
  • AI Safety/reliability
  • LLM/agent capabilities
  • reasoning
  • alignment
  • red teaming
  • bias mitigation

Nice to have

  • large-scale distributed training
  • agentic systems
  • Sovereign AI
  • highly regulated data environments
  • zero-to-one mindset

What the JD emphasized

  • publication track record
  • sovereign AI
  • highly regulated data environments

Other signals

  • leading research into LLM/agent capabilities
  • developing novel methodologies
  • engineering high-stakes AI systems