Robotics Researcher — Manipulation (Omakase Zen)

**Location:** United States (SF Bay Area preferred; US-remote possible)

**Employment:** Full-time

## About Omakase Robotics

We are building the **Toyota of humanoid robots** — humanoids that work reliably in the real world every day, built with mass-production discipline rather than demo discipline.

Three vertically integrated components:

- **Omakase D1** — our own humanoid hardware

- **Omakase Zen** — the manipulation intelligence foundation (this role)

- **Omakase OS** — the orchestration software that runs robots in the field

Our moat is data. Through our own fleet and a partnership with a leading spot-labor platform, we have structurally exclusive access to real-world teleoperation and egocentric human data at a scale competitors cannot replicate. Zen turns that flywheel into manipulation foundation models.

## The Role

You will train the models that make our robots' hands work.

Today that means post-training VLA policies (π0.5-class, ACT) on our own teleoperation data and deploying them onto real robots doing real jobs in Japan. Next it means pre-training a cross-embodiment manipulation foundation model on our proprietary dataset.

You own the loop end to end: data curation → training → evaluation → real-robot deployment → failure analysis → back to data. We do not have a separate team that "puts the model on the robot." That handoff is where robot learning usually goes to die.

## What you would actually work on

Concrete problems currently open on our side:

- **Action-space representation.** Our end-effector-space policies are systematically weaker on rotation than on translation. Until the representation is fixed, EE-space deployment is blocked. This is an open problem we would want your opinion on in the interview.

- **Cross-embodiment transfer.** We collect human hand trajectories and retarget them to the robot. How much of that transfers, and in which representation, is not yet settled by our own measurements.

- **Evaluation that predicts real-robot success.** Open-loop MSE on held-out episodes is cheap and weakly correlated with what happens on the robot. We build sim-based harnesses and structured real-robot trials, and we would like them to disagree less.

- **Making failures legible.** When a policy misses a grasp, the useful question is which part of the pipeline was wrong — the data, the representation, the training, or the hardware. We invest in being able to answer that.

## Responsibilities

- Train and post-train VLA / imitation-learning policies (diffusion and flow-matching action heads, ACT, π-class models) on real teleoperation data

- Design and run the pre-training effort for our cross-embodiment manipulation foundation model

- Build rigorous evaluation: open-loop metrics on held-out real data, sim-based eval harnesses, structured real-robot trials

- Own multi-node distributed training (we operate H100/H200 clusters) — throughput, dataloading, checkpointing

- Work with the data team on dataset schemas, quality gates, and retargeting (EE-space cross-embodiment representations)

- Deploy policies to real robots with the OS team and drive failure analysis back into data and training

## Required Qualifications

We set a high bar here.

- MS/PhD in ML, robotics, or CS — or an equivalent track record that speaks for itself

- **3+ years training large neural models, including at least one substantial robot-learning or VLA project you can walk us through in depth** — what failed, what you measured, what you would do differently

- Demonstrated experience with imitation learning / VLA architectures (π0-class, OpenVLA, RT-class, ACT, diffusion policies). Fine-tuning through an API does not qualify

- Strong PyTorch or JAX; comfortable owning multi-GPU / multi-node training runs end to end

- Experience evaluating policies beyond loss curves: held-out real-data metrics, real-robot success rates, ablations

- Evidence of top-tier work: publications (CoRL / RSS / ICRA / NeurIPS / ICML) **or** policies you shipped onto physical robots in production or serious field trials

## Preferred

- Cross-embodiment training or action-space retargeting (EE-space representations, IK-aware pipelines)

- Teleoperation data collection systems; data-quality tooling for robot datasets

- Sim-to-real and simulation-based evaluation (Isaac, MuJoCo, Genesis)

- World models or video pre-training for robotics

- Japanese is **not** required — Zen operates in English