Research Scientist, Robotics & Models

Innodata(Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked.

Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted  provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.

Progress in physical AI is gated as much by data design as by model architecture. The best manipulation and locomotion policies, vision-language-action models, and world models are only as good as the trajectories, demonstrations, and environments they learn from — so the highest-leverage decisions are made long before a model trains: what to capture, what to synthesize, how to represent an action, and how to know whether any of it will transfer to a real robot.

Innodata is building the data and evaluation practice behind the next generation of robotics foundation models, in direct partnership with the customers and frontier labs defining that frontier. We are hiring a Research Scientist to own the science of that data.

You will partner directly with the customers and frontier labs building robot foundation models, as interested in the data those models learn from as in the models themselves. Your leverage is scientific judgment about data: deciding what is worth capturing in the real world versus generating in simulation, how to weight a training mix across embodiments and sensors, and how to measure whether a policy will hold up outside the lab.

Your conclusions will shape what our partners collect next.

• You will define how Innodata designs, structures, and evaluates data for robot foundation models, and you will validate those choices experimentally. Concretely, you will:

• Translate the requirements of robotics foundation models — vision-language-action models, world models, and manipulation and locomotion policies — into concrete data specifications: modalities, action representations (tokenization, chunking, diffusion and flow‑matching action experts), sampling, annotation schemas, and evaluation criteria.

• Decide what is worth capturing in the real world versus generating in simulation, and curate and weight training mixes across heterogeneous robot datasets that span different embodiments, action spaces, and sensor setups.

• Guide collection across capture modalities — motion capture, egocentric, exocentric, teleoperation, and multi‑sensor — and across synthetic pipelines, so that what we produce maps cleanly to model needs rather than accumulating for its own sake.

• Build evaluation and benchmarking methodology that predicts real‑world transfer — coverage, discriminative power, reliability, and sim‑to‑real fidelity — including world‑model evaluation (rollout quality and action‑conditioned prediction) and the domain‑randomization and system‑identification choices that close the sim‑to‑real gap.

• Run the experiments that prove it: fine‑tune and evaluate foundation models on Innodata data, with data‑quality ablations and scaling studies that demonstrate specific data decisions produce measurable model improvement.

• Design adversarial and long‑horizon evaluations that surface where policies and world models break, and turn those failure modes into better data.

• Publish. Turn what you learn into benchmarks, methodology, and papers that advance the field and earn the trust of the customers and frontier labs we partner with.

• Collaborate with the capture lab, annotation teams, and the…