Vice President of Robotics

As Zeromatter's Robotics Lead, you'll lead our new Robotics team, a team that will sit alongside our Dynamics team which owns Physics Engine development. You won't be starting from zero: robotics is already an active effort at Zeromatter, with policy training and evaluation work underway. You'll define the technical roadmap, grow the team, and work directly with customers on the real manipulation and automation problems they're trying to solve, using those engagements to sharpen both our platform and our policies. You'll be hands-on throughout: training and evaluating policies yourself, and partnering closely with the Dynamics team to ensure simulation fidelity closes the sim-to-real gap.

Responsibilities

• Define and own Zeromatter's technical roadmap for robotics and physical AI, in close partnership with our Dynamics team in service of our northstar: simulation with the fidelity to train and evaluate robotics policies that succeed even where real-world data is scarce, dramatically accelerating how fast robotics policies get developed and deployed.

• 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 including scoping the use case, running proof-of-concept work on our platform, and driving toward outcomes that solve the customer's actual problem.

• Track, apply and improve upon state-of-the-art robot learning research (imitation learning, reinforcement learning, foundation/VLA models, cross-embodiment learning).

• Get hands-on with training, evaluating, and iterating on robot learning policies (imitation learning, reinforcement learning, and/or foundation/VLA models) using Zeromatter's platform and physical hardware, and establish the benchmarks and evaluation methodology to quantitatively demonstrate simulation fidelity, sufficient to both accelerate policy training and evaluate performance with confidence.

• Partner closely with the Dynamics team to identify and prioritize the simulation capabilities (e.g. contact and friction modeling, force/torque data, actuator and sensor fidelity) that matter most for closing the sim-to-real gap.

• Define and help build the user-facing workflows and tooling that let internal teams and customers train, evaluate, and deploy robotics policies on Zeromatter's platform.

Qualifications

• You have an engineering or related technical degree (e.g. robotics, computer science, mechanical/electrical engineering, physics) or equivalent hands-on experience.

• Deep, hands-on experience developing and deploying robot learning systems (imitation learning, reinforcement learning, and/or foundation/VLA policies) on physical hardware gained in industry, in a research lab, or through graduate/postdoctoral work.

• Demonstrated experience taking policies from simulation to real-world deployment, with a strong grasp of domain gap problems.

• A track record of technical leadership including defining roadmaps, setting technical direction, and/or building and growing a team.

• Proficient in Python, Rust, and/or C++.

• Strong communication skills, with the ability to represent technical work credibly to customers, prospects, and other external stakeholders.

• Excited to define a clean-sheet roadmap and shape technical direction at Zeromatter.

• Bonus: Experience with cross-embodiment learning or transferring policies across different robot platforms.

• Bonus: Experience with teleoperation systems and data collection pipelines for robot learning.

• Bonus: Experience working end-to-end across the robotics stack, including perception, classical and learned control systems, task planning, etc.

• Bonus: Familiarity with physics/robotics simulation engines and their role in policy training.

• Bonus: Familiarity with robotic functional-safety standards (e.g. ISO 10218, ISO 13482, ANSI/RIA R15.06) and interest in how simulation can support safety-case development for autonomous, learning-based systems.

• Bonus: Experience characterizing or working with force/torque sensing and contact-rich manipulation tasks.

• Bonus: Publication record or public-facing work in robot learning, embodied AI, or manipulation.

• Bonus: Prior experience in a customer-facing or pre-sales technical role.

• Bonus: Experience building or scaling a technical team from an early stage.