MACHINE LEARNING ENGINEER

Machine Learning Engineer

We run four bimanual robots on the floor of our own factory in Shikahama, Adachi-ku, Tokyo. The factory — not a lab — is where our R&D happens.Hiring criteria ver 1.0Apply for this position

What you would work on

We define three levels of ownership on the ML team. Which one you start at is decided through the interview process.

01

You are given a specific technical theme and improve performance through it

e.g. applying a promising paper, or data-side improvements such as camera tuning

02

You are given a partial goal and reach it — selecting existing techniques and, where needed, proposing new ones

e.g. reducing the failure rate of simple towel grasping during autonomous towel folding

03

Given the goal (raising the revenue a robot generates per hour), you propose what the ML team should do in the first place

e.g. proposing a data collection strategy, or which methods we should be trying

We measure results in profit generated per robot per hour. The experiments we actually run are published openly, including the ones that did not work.

Read our research log

Requirements

  1. 01

    Able to come to our factory in Shikahama, Adachi-ku, Tokyo at least twice a week

    We operate four bimanual robots at our own factory. On-site presence is for attending physical experiments and for in-person discussion. In robotics, improvement ideas often come from seeing the actual environment and behaviour.

  2. 02

    At least one year of professional machine learning experience

    Research conducted as part of a degree does not count.

  3. 03

    Mathematical grounding sufficient to deeply understand the architecture and training methods of VLA models such as pi0.5

    This is needed so that when a newly implemented method does not deliver the expected result, you can diagnose the cause accurately and improve on that basis.

    • Assessed from your submitted assignment and the discussion in the interview
  4. 04

    Able to hold technical discussions in English (spoken)

    Reading papers and writing — Slack posts, reports and so on — assume the use of AI, so we only require spoken communication.

    • Japanese language ability is not required.

Nice to have

  • Experience with deep learning (especially VLA) and robotics
  • Experience with teleoperation and data collection on physical robots
  • Experience operating training infrastructure (multi-GPU training, cloud GPU cost management, data pipelines)
  • A research publication record

Terms

Employment type
Full-time
Annual salary
JPY 8,000,000 – 15,000,000+
Location
Shikahama, Adachi-ku, Tokyo (our own factory)
On-site
At least twice a week
Team
2 ML engineers, 4 data engineers
Compute
10+ in-house GPUs, plus cloud GPUs
Hardware
4 bimanual robots

Hiring process

01

Casual conversation

A conversation with our CEO, Ryosuke Okamae. For mutual understanding — you are not being evaluated.

02

You decide whether to go further

Having heard what the work is, decide whether you want to apply.

03

Submit the assignment

Solve the assignment below and send it to us.

04

Interview (120 minutes)

With Ryosuke Okamae (CEO) and Kanta Sugiyama (Head of ML Engineering).

In the interview we take the assignment you submitted and work through variations of each problem, one at a time, on a whiteboard and out loud. They are not the same questions, but nothing will come from outside the scope of the published assignment. Smartphones, PCs and LLMs cannot be used in that session.

Assignment ver 1.0

This is the assignment you solve and submit before the interview — the real one, not a sample. In the interview we work through variations of these problems. We publish it in full so you can see what we are measuring before you apply. You may use an LLM to solve it.

  • Problem 1

    Linear algebra on the self-attention score matrix, and derivation of its gradients

  • Problem 2

    Conditional expectation of a linear interpolation between two distributions (the flow matching velocity field)

  • Problem 3

    Interpreting real internal experimental data and proposing a research direction from it

Problem 3 uses data from an experiment we actually ran on the RPE (Remove Pillow Envelope) task. The numbers are published exactly as they came out.

Download the assignment ver 1.0 (PDF, 1.5MB)

Apply for this position

It is fine if you are unsure whether you meet the requirements. The casual conversation is not an evaluation.

Talk to us first：Request a casual conversation