Lead research on post‑training methods for generative robot policies, enabling rapid adaptation and continual learning in real‑world robotic systems.
Compensation
The pay range for this position at commencement of employment is expected to be between $158,400 and $227,700 /year for Massachusetts-based roles.
Responsibilities
Design and implement methods for adapting generative robot policies using reinforcement learning, imitation learning, agentic tool use, and related methods.
Develop methods that enable generative robot policies to improve through ongoing interaction with people and the environment, using signals such as demonstrations, interventions, preference feedback, learned rewards, and other human‑driven signals.
Develop rollout‑based learning and exploration strategies that enable robots to efficiently and safely gather useful experience, leveraging multimodal data from demonstrations, teleoperation, interventions, and on‑policy rollouts, in both simulation and on real robot hardware.
Collaborate across teams in perception, control, foundation models, and ML infrastructure to deploy scalable and reproducible research systems.
Publish research outcomes and contribute to the open robotics and embodied AI communities.
Requirements
Ph.D. or M.S. in Robotics, Machine Learning, Computer Vision, or a related field, or equivalent applied research experience.
Develop multi-modal models (VLA, imitation learning, generative AI) to enable robots to autonomously generate actions from demonstrations, real-time sensor data, and natural language commands.
Responsibilities
Develop and implement cutting-edge Vision-Language-Action (VLA) models, generalist robot transformers, and imitation learning algorithms (e.g., diffusion policies) to enable robots to autonomously execute complex tasks.
Design, test, and refine your algorithms to meet the demands of complex real-world autonomy and navigation tasks, with a focus on spatial reasoning and generalization.
Streamline the data collection and training workflow to efficiently expand model capabilities with new tasks and data sources.
Collaborate with the reinforcement learning team to innovate methods that leverage both simulated and real-world data.
Optimize and distill networks for real-time deployment on the edge (e.g. Nvidia Jetson Thor).
Requirements
Master’s degree or higher in a relevant field such as Engineering, Robotics, or Machine Learning
Nice to have: PhD degree in Robotics, Engineering, Computer Science, Machine Learning
Nice to have: Publications at top-tier conferences
Design, implement, train, adapt, and evaluate reasoning and cognition models for unmanned autonomous platforms.
Compensation
Pay within range listed + Bonus + Benefits + Equity
Responsibilities
Design, implement, train, adapt, and evaluate reasoning and cognition models for unmanned autonomous platforms.
Make and validate tradeoffs among foundation-model approaches, including vision-language models (VLMs) and vision-language-action models (VLAs), specialized deep-learning models, and traditional AI/ML methods.
Develop capabilities for interpreting context, reasoning over time, coordinating among agents, and adapting to incomplete or changing information.
Build reproducible training and adaptation workflows using appropriate combinations of self-supervised learning, supervised fine-tuning, imitation learning, reinforcement learning, and distillation.
Define data requirements, training objectives, benchmarks, and evaluation methods that support meaningful comparisons among model approaches.
Requirements
BS/MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, and/or similar degree, or equivalent practical experience
Nice to have: MS or PhD in Computer Science, Electrical Engineering, or similar degree
Nice to have: Optimising and deploying ML models to resource-constrained hardware platforms
Senior Manager, Engineering - Hivemind Reasoning
f7d8749a-72d6-407c-bf98-292ea630c73a · Posted Oct 19, 2026
Pay within range listed + Bonus + Benefits + Equity
Responsibilities
Build & lead the team that designs, trains, adapts, and evaluates reasoning and cognition models intended for deployment across unmanned platforms
Define the technical roadmap for single-agent and multi-agent models, including architecture choices, training methods, data strategy, and evaluation criteria
Advance model capabilities for interpreting context, reasoning over time, coordinating among agents, and adapting to incomplete or changing information
Drive training efforts, including self-supervised learning, imitation learning, reinforcement learning, and distillation
Establish rigorous evaluations in simulation and with real-world data
Requirements
Bachelor's degree
Master's degree
PhD
Nice to have: PhD in Computer Science or related field
Nice to have: optimizing and deploying ML models to resource-constrained hardware
Conduct original research in embodied AI, robotic manipulation, and world modeling—including perception, planning, and control.
Compensation
Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, or relevant education or training. Please note that the compensation details listed in US role postings reflect the base salary only. It does not include discretionary bonus, equity, or benefits.
Responsibilities
Conduct original research in embodied AI, robotic manipulation, and world modeling—including perception, planning, and control.
Develop and evaluate large-scale multimodal models that integrate vision, language, proprioception, and tactile inputs for real-world robotic tasks.
Create and prototype algorithms for dexterous robotic hands, contact-rich manipulation, and adaptive task generalization.
Explore methods for sim-to-real transfer, self-supervised physical interaction learning, and reinforcement learning in dynamic environments.
Collaborate with hardware and systems teams to translate research into real robotic systems—from simulation to physical deployment.
Requirements
Master’s or Ph.D. in Robotics, Computer Science, Artificial Intelligence, Mechanical/Electrical Engineering, or related fields.
Nice to have: Ph.D. (or Ph.D. candidate expecting graduation within 6–12 months).
Nice to have: Track record of publications in top AI or robotics conferences (CoRL, ICRA, IROS, RSS, NeurIPS, CVPR, ICML).
We are looking for a researcher to own how that hand learns. You will bring hand-level dexterity into vision-language-action and world-model policies, build the demonstration-data engine that feeds them, and make touch a first-class input to learned behavior.
Responsibilities
Bring high-degree-of-freedom hand control into vision-language-action and world-model policies: action representation, conditioning on touch and proprioception, pre-training and fine-tuning, and on-robot evaluation.
Shape how demonstrations from human hands and teleoperation are collected, retargeted to the robot hand, filtered, and scaled into training data.
Make tactile and proprioceptive sensing a first-class input to learned policies, from representation and fusion through deployment on hardware.
Set the dexterity learning agenda, build rigorous hardware evaluation, publish where it makes sense, mentor other researchers, and feed what you learn back into the design of future hands.
Requirements
PhD in machine learning, robotics, or a related field, or equivalent experience
Build and deploy machine learning models for dexterity-driven physical AI robots, collaborating with researchers and engineers to integrate ML into production systems.
Responsibilities
Design, develop, and deploy machine learning models and algorithms to address specific business needs and challenges.
Implement scalable and efficient software solutions to integrate machine learning models into production systems.
Optimize and fine-tune machine learning algorithms for improved performance and accuracy.
Conduct thorough testing and validation of machine learning models to ensure robustness and reliability.
Provide technical guidance and mentorship to junior team members.
Requirements
Master’s degree or Ph.D. in Computer Science, Engineering, Mathematics, or a related field.
Senior AI Engineer - VLA Foundation ModelNewRIVR · Zürich, Switzerland
Staff Engineer, Hivemind Reasoningf7d8749a-72d6-407c-bf98-292ea630c73a · San Mateo, US
Research Engineer - Data EvalCalder · Redwood City, US