EN_2015_Machine Learning Engineer (Autonomous Driving — World Model / Video Generation Model Development)
仕事概要
-About the role-
◆This role is open to engineers with a machine learning background who have worked on large-scale development of video generation models or foundation models, as well as the MLOps / data infrastructure, distillation, optimization, and on-device deployment that supports them. (Experience in autonomous driving / CV / robotics is a plus.)◆
At Turing, we are developing an End-to-End autonomous driving model that takes input from vehicle-mounted cameras and directly controls the vehicle.
As we work toward full self-driving, we are in an exploratory phase
— combining imitation learning with the rapid advances in video generation and world model research to actively find what works.
In particular, large-scale pretraining on not only driving data but also general video data (video generation, self-supervised learning, etc.) has been shown to significantly boost downstream task performance (behavior prediction, planning, etc.), and this trend is growing stronger.
With this in mind, Turing is pursuing a World Action Model (WAM) approach — a unified pipeline that spans modern model families such as video generation models, image/video foundation models (e.g., DINOv3, V-JEPA-style concepts), and world models — from training at scale, through validation and downstream task integration, to distillation, quantization, inference optimization, and final deployment on real vehicles.
We are looking for members who can take these ambitious research directions all the way to something that actually works in the real world.
■What you will work on
・ML development centered on WAM (World Action Model) for autonomous driving
・Large-scale pretraining (scaling training with driving data + general video data, etc.)
・Modeling, implementation, and validation using video generation models and world models
・Validation and application of image/video foundation models based on self-supervised learning (e.g., DINOv3, V-JEPA-style, etc.)
・Application to downstream tasks (e.g., behavior prediction, planning), evaluation design, and improvement
・Model compression and acceleration via distillation, quantization, and inference optimization
・Building and improving experimental infrastructure (data pipelines, reproducibility, experiment management, model operations)
・Literature review and implementation validation in related areas (Transformers, robotics, world models, etc.)
■Enjoy being at the frontier of Physical AI
Giving 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. You will need to build unique ML pipelines while leveraging the knowledge already accumulated within the company. We are looking for someone who can drive development in a domain with almost no existing reference points.
■Test your model in the real world
Our development cycle: Build dataset & model → Drive test → Analyze experiment logs → Manage model. You will iterate on your models by experiencing them firsthand in a real vehicle. Use feedback from the physical world to drive your development forward.
■Who is thriving in this role
・Engineers with strengths in robotics, world models, or autonomous driving (behavior prediction, planning, etc.) who have led model development
・Engineers who have pursued large-scale data preprocessing, filtering, and data quality design, and have achieved training reproducibility and scaling in practice
・Engineers from research labs or corporate research teams who have taken exploratory topics all the way from implementation → validation → improvement to tangible results
・Engineers 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
必須スキル
-About the role-
◆This role is open to engineers with a machine learning background who have worked on large-scale development of video generation models or foundation models, as well as the MLOps / data infrastructure, distillation, optimization, and on-device deployment that supports them. (Experience in autonomous driving / CV / robotics is a plus.)◆
At Turing, we are developing an End-to-End autonomous driving model that takes input from vehicle-mounted cameras and directly controls the vehicle.
As we work toward full self-driving, we are in an exploratory phase
— combining imitation learning with the rapid advances in video generation and world model research to actively find what works.
In particular, large-scale pretraining on not only driving data but also general video data (video generation, self-supervised learning, etc.) has been shown to significantly boost downstream task performance (behavior prediction, planning, etc.), and this trend is growing stronger.
With this in mind, Turing is pursuing a World Action Model (WAM) approach — a unified pipeline that spans modern model families such as video generation models, image/video foundation models (e.g., DINOv3, V-JEPA-style concepts), and world models — from training at scale, through validation and downstream task integration, to distillation, quantization, inference optimization, and final deployment on real vehicles.
We are looking for members who can take these ambitious research directions all the way to something that actually works in the real world.
■What you will work on
・ML development centered on WAM (World Action Model) for autonomous driving
・Large-scale pretraining (scaling training with driving data + general video data, etc.)
・Modeling, implementation, and validation using video generation models and world models
・Validation and application of image/video foundation models based on self-supervised learning (e.g., DINOv3, V-JEPA-style, etc.)
・Application to downstream tasks (e.g., behavior prediction, planning), evaluation design, and improvement
・Model compression and acceleration via distillation, quantization, and inference optimization
・Building and improving experimental infrastructure (data pipelines, reproducibility, experiment management, model operations)
・Literature review and implementation validation in related areas (Transformers, robotics, world models, etc.)
■Enjoy being at the frontier of Physical AI
Giving 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. You will need to build unique ML pipelines while leveraging the knowledge already accumulated within the company. We are looking for someone who can drive development in a domain with almost no existing reference points.
■Test your model in the real world
Our development cycle: Build dataset & model → Drive test → Analyze experiment logs → Manage model. You will iterate on your models by experiencing them firsthand in a real vehicle. Use feedback from the physical world to drive your development forward.
■Who is thriving in this role
・Engineers with strengths in robotics, world models, or autonomous driving (behavior prediction, planning, etc.) who have led model development
・Engineers who have pursued large-scale data preprocessing, filtering, and data quality design, and have achieved training reproducibility and scaling in practice
・Engineers from research labs or corporate research teams who have taken exploratory topics all the way from implementation → validation → improvement to tangible results
・Engineers 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
歓迎スキル
■Preferred qualifications
▼Generative & foundation models
・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
▼Distillation & deployment
・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
求める人物像
■Who we are looking for
・Driven to build a world-class company
・Self-starter who takes initiative on everything
・Humble, with genuine empathy for others
・Flexible and excited by rapid organizational and business growth
・Growth-oriented mindset
・Resilient — able to find joy even in tough challenges
◆Tech stack
・Language：Python
・Libraries：PyTorch、Transformers、Lightning、Accelerate、DeepSpeed/FSDP、FlexAttention
・Middleware：Slurm
・Dev environment：Large-scale GPU clusters built on AWS and GCP
・Platform：Jetson、Linux
【LEARN MORE】
▼Company website
https://tur.ing/
▼Turing Tech Blog
https://zenn.dev/p/turing_motors
▼Turipo（Owned media / Web Magazine）
https://tur.ing/turipo
▼Turing TechTalk #7 E2E Autonomous Driving Development Process
https://youtu.be/KHqGVkIhYp4?feature=shared
【Application notes】
· Please submit your resume and work history in PDF format.
· Do not include salary information (current or desired) in your application documents or entry form.
· Our HR team will discuss compensation separately during the selection process.
応募概要
給与
Salary
◾️Engineer
・Expected annual salary: ¥7,000,000 – ¥10,000,000
・Base monthly salary: ¥444,449 – ¥634,925
・Overtime allowance: ¥138,891 – ¥198,415 (※ covers 40 hours/month of deemed overtime)
◾️Senior Engineer
・Expected annual salary: ¥10,000,000 – ¥15,000,000
・Base monthly salary: ¥634,925 – ¥952,380
・Overtime allowance: ¥198,415 – ¥297,620 (※ covers 40 hours/month of deemed overtime)
◾️Principal Engineer
・Expected annual salary: ¥15,000,000 – ¥20,000,000
・Base monthly salary: ¥952,380 – ¥1,269,843
・Overtime allowance: ¥297,620 – ¥396,827 (※ covers 40 hours/month of deemed overtime)
※ The figures above are estimates. The final offer will be determined at the time of acceptance, taking into account your current compensation as well as your experience and skills.
※ For career hires, the floor is generally ¥7,000,000. However, offers exceeding ¥20,000,000 are also under consideration for exceptional candidates.
勤務地
■Heiwajima Office
〒143-0006
Tokyo Ryutsu Center Logistics Building A 6-1-1 Heiwajima, Ota-ku, Tokyo, Japan
▼Access
7-minute walk from Tokyo Monorail Ryutsu Center Station. Commuting by public transit or personal vehicle/motorcycle is permitted. Expressway tolls and fuel costs are reimbursed.
※Commuting allowance
・Allowance is calculated based on the commuting route and method approved by the company, in accordance with company-defined standards.
(The maximum reimbursement amount is set separately by the company. Tax treatment — taxable or non-taxable — is handled appropriately in accordance with the Income Tax Act and other applicable laws.)
・Expressway tolls incurred during commuting are eligible for reimbursement.
・For motorcycle commuters, only fuel costs are eligible for reimbursement. (Expressway tolls are not covered.)
雇用形態
Employment type：Full-time, permanent
勤務体系
◾️Flextime system
・Core hours: 11:00 – 15:00
・Flexible hours: 08:00 – 11:00 and 15:00 – 22:00
・Typical schedule: 10:00 – 19:00 (1-hour lunch break).
※Start and end times can be adjusted flexibly to suit your lifestyle and workload.
【Leave & Holidays】
■Weekends & public holidays
■13 days paid leave granted at joining (first year)
■year-end/new year holidays
■Life support leave: 5 days granted on day of joining (valid 1 year)
■Congratulatory / bereavement leave (incl. 5-day marriage leave)
■Maternity & parental leave
■Reduced working hours for childcare (until end of 3rd grade)
■Child nursing / caregiver leave (5 days, unpaid)
試用期間
Probation：3 months
福利厚生
【BENEFITS】
■Full social insurance (health, pension, employment, workers' comp)
■Commuting allowance: Reimbursed based on the commuting route and method approved by the company, calculated in accordance with company-defined standards.
■Subsidized lunch (company cafeteria program)
■Influenza vaccination subsidy
■Regular health check-up
■Health consultation via partner clinic
■External counseling / EAP service
■PC selection program (engineers)
■Turing Unlimited AI: All directly employed staff can use any AI services required for work at full company expense, with no usage cap.
■Parking subsidy (engineers under 30)
■Book purchase program
■Babysitter discount program
■Free AMH fertility test
■Free women's health counseling (partner clinic)
■Office snack convenience store
■All-hands social event subsidy
■No dress code
■Company resort facility (Tohshinkyou)
■Welfare rental housing service