Machine Learning Engineer, Global Public Sector
Build and evaluate reliable agentic AI systems for high-stakes public-sector applications, spanning applied research, model optimization, safety, and benchmarking. The role requires strong Python and AI infrastructure experience, production engineering rigor, and expertise in LLM evaluation or red-teaming.
About the job
Responsibilities
- Design and build agent architectures, harnesses, tool-use protocols, and logic flows for reliable autonomous agents in complex workflows.
- Lead applied research into reliable multi-step agentic systems and long-horizon reasoning frameworks for national security and public policy.
- Develop rigorous evaluation frameworks, benchmarks, and protocols for safety, bias, and performance in high-stakes environments.
- Conduct red-teaming and develop strategies to mitigate hallucinations in regulated data environments.
- Build agents for autonomous information synthesis and long-horizon reasoning over large datasets.
- Evaluate and adapt models for specialized use cases, including low-resource languages, complex OCR, and GPU-constrained environments.
- Optimize models through context engineering, retrieval-augmented generation, and inference-time techniques.
- Advise public-sector leaders on the practical limits, safety requirements, and performance trade-offs of emerging AI technologies.
Requirements
- Exceptional proficiency in Python and experience building agentic harnesses or AI infrastructure.
- Experience writing modular, scalable, reliable, production-ready code.
- Track record of translating theoretical AI concepts into functional prototypes or products.
- Experience reading research papers and assessing whether their methods are viable for production systems.
- Experience with LLM benchmarking, red-teaming, or evaluations beyond standard academic datasets.
- A Master's or PhD in Computer Science, Mathematics, or a related field focused on machine learning is preferred; demonstrated impact and engineering excellence are also valued.
Nice to Have
- Deep experience building multi-agent systems, including chain-of-thought optimization and tool-calling reliability.
- Experience with highly regulated data environments, on-premise deployments, or sensitive government use cases.
- Knowledge of model-performance optimization for limited GPU capacity or specific latency requirements.
- Comfort navigating ambiguity and defining research directions from scratch.
Compensation and Benefits
- Successful Qatar-based candidates will receive support from Scale with the visa application process, subject to approval by Qatari authorities.
Skills
Python, LLMs, Agentic Systems, Multi-Agent Systems, RAG, Llm Benchmarking, Red Teaming, Ai Safety, Model Optimization, Inference Optimization, Ocr, Tool Calling, Long-Horizon Reasoning, Evaluation Frameworks, On-Premise Deployment
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