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 tooling for researchers
• Data pipelines and sourcing infrastructure for enterprise robotics and AI labs
• Cross-embodiment learning from large, real-world datasets

We are looking for a Founding Engineer to 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.

You will operate at the intersection of data systems, cloud infrastructure, and robotics learning.

• Design and build high-throughput data pipelines for ingesting, processing, and standardizing robotics datasets
• Architect distributed systems and microservices for robotics data processing and dataset infrastructure
• Develop the data compiler layer that standardizes raw logs into a unified representation
• Build cross-embodiment transformation pipelines (retargeting, normalization, alignment)
• Integrate multimodal augmentation models (vision, language, SLAM, simulation)
• Enable real ↔ sim pipelines and unified evaluation frameworks
• Build tooling for: dataset ingestion & validation, annotation and enrichment, and dataset versioning and reproducibility
  

• Public dataset platform (APIs, SDKs, data loaders)
• Internal pipelines for enterprise data sourcing and validation
• Interfaces for model training and evaluation
  

• Helping define the architecture for robotics dataset infrastructure and pipelines
• Working directly with the founding team on product and technical direction
  

Neural 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 infrastructure
• Supporting experiments around cross-embodiment datasets

We are open to two strong profiles, ideally combined:

• Experience building large-scale data systems (TB–PB scale)
• Strong background in:
• distributed systems
• streaming pipelines
• microservices architecture
• Hands-on with tools such as:
• Kafka / Pulsar
• Temporal / Airflow / workflow orchestration
• AWS (S3, SQS, Lambda, ECS/EKS) / GCP equivalents
• Experience designing robust, fault-tolerant pipelines
• Strong backend engineering skills (Python, Go, or similar)
  

• Experience in robot learning / embodied AI / manipulation
• Familiarity with:
• VLA / world models
• imitation learning / RL
• dataset design for robotics
• Strong understanding of:
• kinematics (FK/IK)
• retargeting across embodiments
• coordinate frames and calibration
• Experience working with:
• ROS / URDF
• simulation tools (Isaac Gym, MuJoCo, etc.)
• Good intuition for what makes high-quality robotics data
  

• Driven by the mission to define new, foundational infrastructure for an entire field
• Has experience in both infrastructure and robotics, or has worked closely across both
• Thinks in systems: not just models or pipelines, but how everything composes
• Cares deeply about data quality, structure, and scalability
• Comfortable working in an ambiguous, fast-moving environment
  

• Experience with large multimodal datasets (video, sensor logs, etc.)
• Experience with dataset platforms (HuggingFace, TFDS, RLDS, etc.)
• Experience building internal tools for ML/data teams
• Exposure to simulation ↔ real transfer systems
• Startup or zero-to-one experience

San Francisco/Remote

Compensation for this role includes:

• $150,000 – $220,000/yr base salary + meaningful early-stage equity