ML Engineer, Agents & Reasoning

This role builds agentic ML systems that reason, plan, and act inside real materials discovery workflows, turning predictive models into reliable, operational decision-making agents that work with messy physical experiments. You'll join a cross-functional team at the intersection of AI, engineering, and laboratory automation to embed autonomy, safety, and observability into discovery pipelines. Your work matters because it enables robust, uncertainty-aware decisions that accelerate scientific progress while keeping humans in the loop.

What youll do

• Design and implement agentic systems that plan, reason, and act across materials discovery workflows.
• Build agentic decision-making systems that operate over experiments, simulations, and scientific datasets.
• Select next actions under uncertainty and encode when autonomy should act versus when humans stay in the loop.
• Implement planning, control logic, and uncertainty-aware decision-making tailored to physical systems.
• Encode operational, experimental, and safety constraints directly into agent behavior.
• Define stopping criteria, fallback strategies, and recovery mechanisms to prevent brittle behavior.
• Collaborate with AI researchers to embed predictive models into agent workflows and turn models into executable actions.
• Integrate agents with lab, automation, and software systems so outputs translate into real-world actions.
• Instrument agents with logging, monitoring, and diagnostics for observability and debugging.
• Build evaluation frameworks that assess decision quality, learning efficiency, and system behavior beyond model accuracy.
• Analyze failure cases and iterate on system design based on real-world outcomes.
• Take ownership of systems from prototype through deployment and ongoing operation.

What Dunia Innovations is looking for

• 4–8 years of experience building ML-driven or algorithmic decision-making systems in production or applied research settings.
• Strong background in scientific or structured data modeling rather than language-first systems.
• Experience with planning, control, optimization, probabilistic reasoning, or decision-making under uncertainty.
• Proficiency in modern ML frameworks (e.g., PyTorch, JAX) and strong general software engineering skills.
• Comfortable owning systems end-to-end, from prototype to reliable operation.
• Ability to reason clearly about system behavior in complex, partially observable environments.
• Technically curious with interest in physical systems, experiments, and real-world constraints.
• Clear communicator who can work effectively across AI, engineering, and scientific teams.
• English fluency; additional language skills desirable.