Research Engineer, Robotic Learning
Job Description: Design simulation environments for NEO (name of robot) and teach NEO (robot) to learn new capabilities via reinforcement learning (RL) algorithms. This enables our robots to be safe and robust in a variety of conditions. Full-stack engineering, from data engineering to model architecture design to shipping polished products. Train NEO to do a diverse set of manipulation and locomotion tasks. Close the sim2real gap between policies trained in simulation and real. Work with controls, Quality Assurance (QA,) and data collection teams to ship reinforcement learning (RL) policies to the production fleet. Deploy skills trained with reinforcement learning (RL) into home environments.
Requirements: Bachelor’s degree or foreign degree equivalent in Computer Science, Mathematics, Software Engineering, Robotics, or related field and four (4) years of experience in Software Engineering or related role.
Skills: Experience and/or education must include:
Train large-scale imitation-learning models for motion planning. Curate expert demonstration datasets, designing loss functions, and deploying policies in real-world or simulated environments. Implement DAgger (Dataset Aggregation), including iteratively collecting additional expert demonstrations in response to model errors, to reduce covariate shift and improve policy robustness.
Generative architectures such as diffusion models, variational autoencoder, or applied to planning. Model architecture selection and stable training on high-dimensional data.
Architecting and executing distributed neural-network training across large GPU clusters. Data/model parallelism, gradient synchronization such as NCCL, fault tolerance, and efficient I/O for multi-TB datasets.
Build and optimize low-latency, real-time software systems. Use of C++ for hard-real-time applications, with hands-on knowledge of multithreading, lock-free data structures, and real-time OS constraints.
CONTACT: Please reference Job ID: 9443267 & email resume to careers.us@1x.tech
Skills: Experience and/or education must include
- Train large-scale imitation-learning models for motion planning. Curate expert demonstration datasets, designing loss functions, and deploying policies in real-world or simulated environments. Implement DAgger (Dataset Aggregation), including iteratively collecting additional expert demonstrations in response to model errors, to reduce covariate shift and improve policy robustness.
- Generative architectures such as diffusion models, variational autoencoder, or applied to planning. Model architecture selection and stable training on high-dimensional data.
- Architecting and executing distributed neural-network training across large GPU clusters. Data/model parallelism, gradient synchronization such as NCCL, fault tolerance, and efficient I/O for multi-TB datasets.
- Build and optimize low-latency, real-time software systems. Use of C++ for hard-real-time applications, with hands-on knowledge of multithreading, lock-free data structures, and real-time OS constraints. CONTACT: Please reference Job ID: 9443267 & email resume to careers.us@1x.tech
- How much does the Research Engineer, Robotic Learning at 1X Technologies pay?
- The posting lists a range of $167K–$350K per year. Ranges reflect what 1X Technologies publicly declared on the source posting.
- Where is this Research Engineer, Robotic Learning role based?
- The role is based in San Carlos, CA.
- What experience does 1X Technologies expect for this role?
- The posting is tagged as a lead-level role, typically 7+ years of experience. Check the requirements section for specifics.
- Where is 1X Technologies headquartered?
- 1X Technologies is headquartered in Moss, Norway.
- How was this posting sourced?
- This role was pulled directly from 1X Technologies's Ashby careers site. Apply links open in the employer's own ATS — no reposts or aggregator middleware.
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