Data Quality & Evaluation Engineer, World Models

GaussMathematics is building a world data infrastructure for models that understand the world.

We work with virtual worlds, simulation systems, and physical environments to produce structured multimodal data engines for world models, robotics, and physical AI. Our work includes world-state snapshots, synchronized multi-view video, action-state trajectories, replayable traces, benchmark tasks, evaluation records, and customer-specific data pipelines.

We work with frontier teams building the next generation of spatial intelligence, embodied AI, robotics systems, generative video, simulation, and physical AI.

We are looking for a Data Quality & Evaluation Engineer, World Models to build validation, benchmark, QA, and evaluation systems for world data infrastructure.

This is an entrepreneurial, high-ownership engineering role at the intersection of data quality, model evaluation, benchmark design, QA systems, and frontier AI. You will work on systems that determine whether complex multimodal data is complete, consistent, reliable, and useful for training and evaluating models.

This is not a conventional QA role. The work requires technical judgment, structured thinking, and the ability to reason about data quality in environments where state, time, actions, physics, and outcomes all matter.

You will help define how world data should be validated, benchmarked, and evaluated.

• Build validation and QA systems for structured multimodal datasets and customer-specific data engines.
• Design benchmark tasks, evaluation slices, review workflows, and quality criteria for world model data.
• Work with world-state snapshots, synchronized video, object metadata, action traces, simulation records, and evaluation outcomes.
• Create tools for detecting missing fields, inconsistencies, temporal misalignment, schema drift, annotation issues, and data regressions.
• Work closely with infrastructure, ML, simulation, robotics, and customer delivery teams to improve data quality.
• Help define what “good data” means for world models, robotics, spatial intelligence, and physical AI.
• Build reporting and inspection workflows that make data quality visible and actionable.
• Support confidential customer programs with precision and reliability.

• Strong interest in data quality, evaluation, benchmarks, AI systems, world models, robotics, or multimodal data.
• Ability to reason carefully about complex technical systems and edge cases.
• Experience building QA tools, validation systems, evaluation pipelines, data workflows, or testing infrastructure.
• Comfort working with structured and unstructured data across video, metadata, state, actions, annotations, and logs.
• Strong attention to detail and ability to communicate issues clearly.
• Good judgment in ambiguous, early, and customer-specific technical settings.
• A builder mindset: you are excited to create the quality system, not just check outputs manually.

• Experience with ML evaluation, dataset QA, benchmark design, annotation quality, data validation, or model review workflows.
• Familiarity with computer vision, robotics, simulation data, multimodal datasets, world models, or physical AI.
• Experience with Python, SQL, data validation frameworks, internal tools, dashboards, or workflow automation.
• Experience designing test sets, evaluation criteria, labeling guidelines, or quality review systems.
• Experience in an early-stage company, AI lab, data platform team, or 0–1 technical environment.

Success in this role means building the systems that make world data trustworthy, measurable, and useful for frontier AI teams.

You will help GaussMathematics deliver data engines with the quality, consistency, and evaluation structure that advanced models require.

GaussMathematics is working on a foundational problem: how to turn complex environments into structured data systems for models that need to understand, predict, and act in the world.

This is an opportunity to work at the center of data quality, model evaluation, and world data infrastructure.

Send a note to:

walkingtogether@gaussmathematics.ai

Please include your background, relevant experience, examples of data quality, evaluation, benchmark, QA, or technical systems you have built, and anything else that helps us understand what makes you different.