ML Engineer, Agents & Reasoning

Pegel; Berlin; Hybrid; EUR 80,000 - 120,000; Full time

Job summary

Dunia seeks an ML Engineer, Agents & Reasoning, to design and build agentic AI systems that reason, plan, and act within messy materials discovery workflows. You will ensure agents can operate across experiments, simulations, and datasets and escalate when needed.

You will collaborate with AI researchers and engineering teams to deploy robust, production-grade systems with strong observability and maintenance, enabling reliable real-world decision-making.

Qualifications

• 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.
• Experience with planning, control, optimization, probabilistic reasoning, or decision-making under uncertainty.
• Proficiency with PyTorch or JAX and solid software engineering skills.

Responsibilities

• Build agentic decision-making systems for discovery, grounding reasoning in physical and scientific reality.
• Collaborate with AI researchers to embed predictive models into agent workflows and connect to real experimental systems.
• Translate research concepts into robust, maintainable ML systems with observability and diagnostics.
• Measure evaluation quality, learning efficiency and system behavior beyond model accuracy; iterate from failures to improvements.
• Take ownership of systems from prototype to deployment and operation.

Skills

ML systems

Decision making

Planning & control

Probabilistic reasoning

PyTorch / JAX

Software engineering

Communication

English fluency

Tools

Python

JAX

PyTorch

Job description

Your mission

Build agentic AI systems that reason, plan, and act inside real materials discovery workflows.

Most agent systems live in clean environments: browsers, codebases, or synthetic benchmarks. At Dunia, agents must reason about messy reality: experiments that fail, data that contradicts itself, and physical systems that don’t reset cleanly.

As ML Engineer, Agents & Reasoning, you build the systems that make AI act responsibly inside that reality. You design agents that decide what to do next, use tools intelligently, recover from failure, and know when they don’t know.

Your work sits at the boundary between cognition and control.

Your tasks will include:

Build agentic decision-making systems for discovery

• Design and implement agentic systems thatplan, reason, and actacross materials discovery workflows
• Develop agents thatoperateoverexperiments, simulations, and scientific datasets, selecting next actions under uncertainty
• Define how autonomy is scoped, when humans stay in the loop, and how decisions are escalated

Ground reasoning in scientific and physical reality

• Implement planning, control logic, anduncertainty-aware decision-makingtailored 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

Turn models into action

• Collaborate closely with AI researchers to embed predictive models into agent workflows
• Work with lab, automation, and software teams to connect agents to real experimental and simulation systems
• Ensure agent outputs translate into executable actions, not just recommendations

Measure what matters

• Build evaluation frameworks that assessdecision quality, learning efficiency, and system behavior, not just model accuracy
• Analyze failure cases and iterate on system design based on real-world outcomes
• Help define what “good decisions” mean in scientific discovery contexts

Ship reliable, production-grade systems

• Translate research concepts intorobust, maintainable ML systems
• Instrument agents with logging, monitoring, and diagnostics for observability and debugging
• Take ownership of systems from prototype through deployment and operation

Your profile

• 4–8 years of experiencebuilding ML-driven or algorithmic decision-making systems in production or applied research settings
• Strong background inscientific or structured data modeling, rather than language-first systems
• Experience withplanning, 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
• Able 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 desirable