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.
Work with omni/omni-live to extract animation, fine tune models to generate real time behavior and expression in response to stimulus and internal motivation of the character.
Design, train and evaluate reinforcement learning and imitation learning algorithms for lifelike movement, gaze and posture, conditioned on the robot's affective and motivational state.
Work with animators to convert a library of hand-authored reference motion into style-conditioned policies, using adversarial motion priors and related techniques, so that character can be authored rather than hand-tuned.
Create scenario suites, batch simulation, video capture, and automated critics that assess behavior reads correct to an observer.
Research Scientist Intern, Humanoid Control & Dexterous Manipulation (PhD)
PhD-level internship focused on robotic simulation and control, specifically for humanoid control and dexterous manipulation within AI and AR/VR technology.
Responsibilities
Independently design, develop, and analyze novel research ideas related to robotic simulation and control, with a focus on loco-manipulation and dexterous manipulation.
Deploy, test, and debug algorithms on real robots for sim-to-real transfer.
Collaborate with other researchers and engineers across multiple disciplines.
Communication of research agenda, progress and results to a diverse audience regularly.
Publish research results at top conferences and release code that contributes to the field of robotics.
Requirements
Ph.D. degree in robotics, computer graphics, computer vision, machine learning, or a related STEM field
Lead the new Robotics team at Zeromatter, defining technical roadmap, growing the team, and partnering with customers to develop and deploy robot learning policies.
Responsibilities
Define and own Zeromatter's technical roadmap for robotics and physical AI
Grow Zeromatter's Robotics team; setting the technical bar, hiring, and mentoring as the group scales
Partner directly with customers to understand the real manipulation and automation problems they're trying to solve, and own those engagements end-to-end
Track, apply and improve upon state-of-the-art robot learning research
Get hands-on with training, evaluating, and iterating on robot learning policies using Zeromatter's platform and physical hardware
Requirements
engineering or related technical degree (e.g. robotics, computer science, mechanical/electrical engineering, physics) or equivalent hands-on experience
Nice to have: cross-embodiment learning
Nice to have: teleoperation systems
Nice to have: data collection pipelines for robot learning
Lead research in robot learning, developing state‑of‑the‑art models and collaborating with top academic partners.
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
Work as part of a dynamic, closely-knit research team pushing the bounds of what robots can do.
Study and advance the science of robot learning models, including language, video, action, and multimodal data.
Implement, extend, and create state-of-the-art methods for robot behavior learning, drawing from techniques such as reinforcement learning, imitation learning, and related methods.
Be a key member of the team and play a critical role in rapid progress measured by both the development of internal capabilities and high-impact external publication.
Collaborate with internal research scientists, engineers, and partner labs at top academic research universities to drive pioneering research at scale.
Requirements
PhD in computer science, machine learning, robotics, or a closely related field
Lead the technical implementation and architecture of advanced robotic systems, translating complex AI algorithms into production-ready hardware and software solutions while defining the long-term robotics research roadmap.
Locations (2)
Pittsburgh, Pennsylvania, United States
Sunnyvale, California, United States
Compensation
The U.S. base salary range for this full-time position in Pittsburgh, PA is $290,000-$320,000.
Responsibilities
Drive End-to-End Robotics Deployment from concept to real-world operating environments.
Define and drive the robotics research roadmap in perception, manipulation, motion planning, and AI/ML.
Make key decisions regarding system architecture, hardware-software co-design, and make-or-buy strategies.
Foster an agile and pragmatic engineering culture focused on rapid iteration and continuous testing.
Mentor and coach researchers across mechanical engineering, electronics, software, and AI.
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