Data Infra - Signals and Evaluations (IC)

The role

This individual contributor will build datasets, benchmarks, environments, and evidence to make models and AI systems scientifically defensible. The role involves working across data curation, simulation, learning environments, and model evaluation.

What you will do

• 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 what has been tested and what evidence supports release.

Required qualifications

• 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.
• Software engineering discipline to build tested modules, data contracts, or services.
• Experience converting qualitative model or agent failures into reproducible datasets, tests, or environment scenarios.
• Ability to reason across the complete data path and identify how acquisition, transformation, indexing, orchestration, and presentation affect correctness.

Nice to have

• Experience with dataset curation, benchmark platforms, deep reinforcement learning, world models, synthetic data, multimodal evaluation, or scientific validation.
• Experience with PyTorch or JAX, distributed evaluation, experiment tracking, environment frameworks, or model and agent observability.
• Experience with uncertainty quantification, calibration, out-of-distribution detection, conformal methods, or selective prediction.
• Experience evaluating models deployed to aircraft, satellites, robotics, industrial systems, or other constrained environments.

Benefits

• Competitive compensation based on experience
• Early-stage equity package
• 100% employer-paid health, dental, and vision coverage
• Opportunity to work on novel sensing, data, and AI systems with real-world deployment paths

Before you apply

• Applicants must be a U.S. citizen or national, a lawful permanent resident (green card holder), or eligible to obtain required authorizations from the U.S. Department of State to comply with U.S. expo

About the company

Matter is building the future of vision AI by pairing a sensor that sees molecular chemistry, temperature, and 3D shape with a Large World Model. The team has delivered technologies to Mars for NASA/JPL and designed advanced sensors for U.S. Defense.