Founding Engineer - Robotics Data Infrastructure

Neural Motion is an early-stage robotics startup building infrastructure for robotics datasets and cross-embodiment robot learning.Today, robotics data is fragmented across embodiments, formats, and pipelines. This prevents models from learning shared priors and blocks the scaling we've seen in language and vision. Our goal is to fix this by building a universal data pipeline and cross-embodiment representation layer that unifies real-world logs, simulation, and multimodal datasets into a single, composable system.This platform will power:Public datasets and toolingfor researchersData pipelines and sourcing infrastructurefor enterprise robotics and AI labsCross-embodiment learning from large, real-world datasetsWe are looking for a Founding Engineerto own and drive core parts of this system—from large-scale data pipelines to embodiment-aware transformations. As one of the earliest engineers, you will be at the forefront of the mission that allows knowledge learned in one robot to be reused across many, something that revolutionizes the physical AI world.What You'll Work OnYou will operate at the intersection of data systems, cloud infrastructure, and robotics learningCore AreasDesign and build high-throughput data pipelinesfor ingesting, processing, and standardizing robotics datasetsArchitect distributed systems and microservicesfor robotics data processing and dataset infrastructureDevelop the data compiler layerthat standardizes raw logs into a unified representationBuild cross-embodiment transformation pipelines(retargeting, normalization, alignment)Integrate multimodal augmentation models(vision, language, SLAM, simulation)Enable real ? sim pipelinesand unified evaluation frameworksBuild tooling for: dataset ingestion & validation, annotation and enrichment, and dataset versioning and reproducibilityProduct SurfacesPublic dataset platform (APIs, SDKs, data loaders)Internal pipelines for enterprise data sourcing and validationInterfaces for model training and evaluationTechnical OwnershipHelping define the architecture for robotics dataset infrastructure and pipelinesWorking directly with the founding team on product and technical directionResearch CollaborationNeural Motion is actively exploring research directions in cross-embodiment robot learning and dataset representations. You will collaborate with robotics researchers working on these problems and help translate research results into practical infrastructure and tooling.Integrating research outputs from robotics learning experiments into platform infrastructureSupporting experiments around cross-embodiment datasetsWho You AreWe are open to two strong profiles, ideally combined:(A) Infrastructure / Distributed Systems EngineerExperience building large-scale data systems(TB–PB scale)Strong background in:distributed systemsstreaming pipelinesmicroservices architectureHands-on with tools such as:Kafka / PulsarTemporal / Airflow / workflow orchestrationAWS (S3, SQS, Lambda, ECS/EKS) / GCP equivalentsExperience designing robust, fault-tolerant pipelinesStrong backend engineering skills (Python, Go, or similar)(B) Robotics / Robot Learning EngineerExperience in robot learning / embodied AI / manipulationFamiliarity with:VLA / world modelsimitation learning / RLdataset design for roboticsStrong understanding of:kinematics (FK/IK)retargeting across embodimentscoordinate frames and calibrationExperience working with:ROS / URDFsimulation tools (Isaac Gym, MuJoCo, etc.)Good intuition for what makes high-quality robotics dataIdeal CandidateDriven by the mission to define new, foundational infrastructure for an entire fieldHas experience in both infrastructure and robotics, or has worked closely across bothThinks in systemsnot just models or pipelines, but how everything composesCares deeply about data quality, structure, and scalabilityComfortable working in an ambiguous, fast-moving environmentBonus PointsExperience with large multimodal datasets (video, sensor logs, etc.)Experience with dataset platforms (HuggingFace, TFDS, RLDS, etc.)Experience building internal tools for ML/data teamsExposure to simulation ? real transfer systemsStartup or zero-to-one experienceLocation / SetupSan Francisco/RemoteCompensation / EquityCompensation for this role includes:• $150,000 – $220,000/yr base salary + meaningful early-stage equity