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Build scalable data infrastructure and training pipelines to feed human-demonstration data into RAI. Train and evaluate deep-learning models, collaborate with RAI, hardware, and data teams, and deploy research results to production robots.

On-site intern supporting the engineering team with general assistant tasks: operations support, errands, logistics, and ad-hoc help to keep the team running smoothly.

Own the industrial design and wearability of the Neural Gambit sleeve and clip-on hub. Design SLS and soft-goods for all‑day comfort, prototype and fit-test on real arms, collaborate with 3D/content for renders, and balance refined technical-product aesthetics with manufacturability.

On-site full-time video editor responsible for cutting and finishing REVEL's story, owning the edit pipeline, and delivering fast turnarounds for social. Gear provided. Collaborate with engineering teams in the Czech Republic.

Design, train, and evaluate vision-language-action control models using human-demonstration data; build scalable, reproducible training and evaluation pipelines; collaborate with RAI, hardware, and data teams; and deploy research results to production robots.

Lead production CAD and 3D modeling for the Neural Gambit wearable, humanoid robot, and content renders. Create exploded views, cross-sections, hero renders, and support industrial design and content teams for hardware visualization and film assets.

Build and own industrial and technology partnerships: source and close industrial POC partners, manage existing partnerships (Vorwerk, OT Bioelettronica, RealSense), and support the fundraising narrative for REVEL's AI robotics products.

Build and maintain the data and services backbone for capture, training, and deployment of humanoid robots. Design large-scale data pipelines and storage, build internal APIs and services, and own reliability and scalability of cloud and data infrastructure.

Develop and evaluate deep-learning models mapping HD-sEMG and tactile signals to robot force using human-demonstration data. Build scalable, reproducible training and evaluation pipelines, collaborate with RAI, hardware, and data teams, and deploy research models to production on physical robots.

Train and evaluate models on human-demonstration data, build training and evaluation pipelines, and deploy learned policies to physical humanoid robots.