Data Infra - Signals and Evaluations (IC)

1 мес. назад

СШАMiddleOnsite

dataset workflowdata curationbenchmarkingsimulationreinforcement learningevaluation

Individual contributor role focused on building datasets, benchmarks, simulation environments, and evaluation infrastructure for AI systems.

О компании

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 built core infrastructure at OpenAI. We are now building the next generation of space- and airborne-based sensing systems.

Обязанности

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.
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.
Model and agent releases are supported by reproducible datasets, benchmarks, and clearly stated evidence.
Failures from experiments and deployments become durable test cases that improve future systems.
Evaluation results preserve scientific meaning and make capability, uncertainty, and residual risk legible to the team.

Требования

To comply with U.S. export regulations, applicants must be one of the following:
A U.S. citizen or national
A lawful permanent resident (green card holder)
Eligible to obtain required authorizations from the U.S. Department of State

Будет плюсом

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.

Условия

This role is based in San Francisco, CA, and requires onsite work.
At Matter, we believe in rewarding high performance and providing the support you need to thrive. Our compensation and benefits package includes:
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 to the largest industries in the world

Другое

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.
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.