About Matter Intelligence

Welcome to Matter, where we are building the future of vision AI: pairing a world-first sensor that sees molecular chemistry, temperature, and 3D shape with a Large World Model that will be the most powerful intelligence engine for the physical world. This system doesn't just see what something looks like; it understands everything from a single pixel. We call this Superintelligent Vision.

Our team has delivered technologies to Mars for NASA/JPL, designed advanced sensors for U.S. Defense, and frontier artificial intelligence systems. We are now building the next generation of space- and airborne-based sensing systems.

About the Role

Matter is hiring a Signal, Data, and Evaluation Engineer to build the datasets, benchmarks, environments, and evidence that make our models and AI systems scientifically defensible. Reporting to Ignacio Cases Martin, this individual contributor will work across data curation, simulation and learning environments, model and agent evaluation, and release-quality evidence.

Key Responsibilities

• Build versioned dataset workflows for collection, curation, filtering, labeling, ground truth, synthetic data, hard-negative mining, contamination controls, and train-evaluation isolation.
• Design benchmark suites across perception, multimodal reasoning, physics-informed prediction, retrieval, planning, tool use, world modeling, and customer tasks.
• Create simulation and environment infrastructure for reinforcement learning, imitation learning, offline learning, model-based learning, and agent training.
• Define repeatable evaluation methods across accuracy, calibration, generalization, robustness, safety, latency, cost, and operational outcomes.
• Turn field, mission, and customer failures into versioned datasets, benchmark cases, adversarial tests, and environment scenarios.
• Build automated promotion gates and communicate clearly what has been tested, what remains unproven, and what evidence supports release.

Qualifications

Required

• Experience in ML evaluation, dataset engineering, reinforcement-learning environments, simulation, scientific ML, test infrastructure, or production AI systems.
• Strong foundations in statistics, experimental design, benchmark validity, distribution shift, calibration, reward design, and evidence-based acceptance.
• Fluency in Python and modern machine-learning frameworks, with the software engineering discipline to build tested modules, data contracts, or services.