Machine Learning Engineer (Autonomous Driving — World Model / Video Generation Model Development)

What you will work on

1. 1ML development centered on WAM (World Action Model) for autonomous driving
2. 2Large-scale pretraining (scaling training with driving data + general video data, etc.)
3. 3Modeling, implementation, and validation using video generation models and world models
4. 4Validation and application of image/video foundation models based on self-supervised learning (e.g., DINOv3, V-JEPA-style, etc.)
5. 5Application to downstream tasks (e.g., behavior prediction, planning), evaluation design, and improvement
6. 6Model compression and acceleration via distillation, quantization, and inference optimization
7. 7Building and improving experimental infrastructure (data pipelines, reproducibility, experiment management, model operations)
8. 8Literature review and implementation validation in related areas (Transformers, robotics, world models, etc.)

Enjoy being at the frontier of Physical AI

1. 1Giving AI a physical presence and enabling it to deliver value in the real world — autonomous driving is exactly where humanity is pushing this frontier today.
2. 2You will need to build unique ML pipelines while leveraging the knowledge already accumulated within the company.
3. 3We are looking for someone who can drive development in a domain with almost no existing reference points.

Test your model in the real world

1. 1Our development cycle: Build dataset & model → Drive test → Analyze experiment logs → Manage model.
2. 2You will iterate on your models by experiencing them firsthand in a real vehicle.
3. 3Use feedback from the physical world to drive your development forward.

Who is thriving in this role

1. 1Engineers with strengths in robotics, world models, or autonomous driving (behavior prediction, planning, etc.) who have led model development
2. 2Engineers who have pursued large-scale data preprocessing, filtering, and data quality design, and have achieved training reproducibility and scaling in practice
3. 3Engineers from research labs or corporate research teams who have taken exploratory topics all the way from implementation → validation → improvement to tangible results
4. 4Engineers who can quickly catch up with the work of leading researchers and recent papers, reproduce and extend them, and connect the results to product or on-vehicle validation

Who we are looking for

1. 1Driven to build a world-class company
2. 2Self-starter who takes initiative on everything
3. 3Humble, with genuine empathy for others
4. 4Flexible and excited by rapid organizational and business growth
5. 5Growth-oriented mindset
6. 6Resilient — able to find joy even in tough challenges

応募資格（必須）

• Practical experience in model development using machine learning / deep learning
• Experience implementing and operating training code using PyTorch or similar
• Understanding of Transformer-based architectures, with experience implementing or modifying them
• Foundational understanding of large-scale training (distributed training, optimization, training stabilization, experiment management), with hands-on experience in at least one area
• Ability to read research papers and technical documents, and reproduce / validate their findings
• Japanese language proficiency (JLPT N2 or equivalent)

歓迎する経験

• Exploration of video generation model training recipes (Diffusion / Flow Matching, long-sequence training, self-forcing-style recovery training)
• Research and development experience with image/video tokenizers for generative modeling downstream tasks
• Experience with pretraining or transfer learning of image/video foundation models via self-supervised learning (e.g., DINOv3, V-JEPA, etc.)
• Large-scale pretraining for robot tasks using Latent Action Models
• Research and development related to visual geometry foundation models such as Feed-Forward 3DGS or DepthAnything3
• Experience with model compression techniques including distillation, quantization, and inference optimization
• Experience fine-tuning and distilling video generation models for robot tasks and deploying them to real hardware
• Experience meeting inference requirements (latency / throughput / memory) under constraints such as in-vehicle or edge environments

待遇・福利厚生

• AIツール利用
• All-hands social event subsidy
• Company resort facility (Tohshinkyou)
• PC支給
• Welfare rental housing service
• インフルエンザ予防接種
• ベビーシッター割引券
• 女性健康相談
• 社外相談機関提携
• 駐車場代補助制度

求めるスキル・経験