EN_2010_Machine Learning Engineer（E2E Autonomous Driving Model Development）

• EN_2010_Machine Learning Engineer（E2E Autonomous Driving Model Development）
• 仕事概要

◆This role is open to ML engineers with expertise in one or more of the following domains

Autonomous Driving, Computer Vision, or Machine Learning.

At Turing, we are developing an End-to-End autonomous driving ML model

— a single machine learning model that takes input from vehicle-mounted cameras and directly outputs vehicle control commands.

Autonomous driving model development is a true multi-disciplinary challenge, spanning far beyond machine learning alone. There are many areas to contribute:

data collection, dataset creation (data quality improvement, calibration, coordinate transforms), and model training (architecture design, training efficiency improvements), among others.

We are looking for engineers with a background in autonomous driving

— as well as engineers with outstanding expertise from software, robotics, or other industries.

Let's tackle one of humanity's grand challenges together.

◾️What you will work on

We work on a wide range of problems — not just model architecture improvements, but also data quality and quantity challenges.

The examples below are just a subset; if any of them resonate with your experience, we encourage you to apply.

◆Example：

・Implementation of End-to-End autonomous driving models

・Planning and strategy for data collection

・Dataset creation and improvement

-Auto-labeling model implementation and improvement

-Camera and sensor calibration

・Implementation of model training algorithms

・Optimization and speed-up of model training code

・On-vehicle model evaluation and experiment management

・Research, reproduction, and implementation of state-of-the-art papers

◾️Our development approach

We pursue both data-centric and model-centric approaches in parallel. Challenges arise from many angles — data quality issues with various root causes, architecture and backbone exploration — giving the team a wide solution space to work in. We also run large-scale training jobs on GPU clusters, so optimizing training code for speed is an active area of focus.

E2E autonomous driving is still an open problem. The model you build could become the industry standard for the next generation of self-driving systems.

【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 — not just on paper. Use feedback from the physical world to drive your development forward.

必須スキル

◆This role is open to ML engineers with expertise in one or more of the following domains

Autonomous Driving, Computer Vision, or Machine Learning.

At Turing, we are developing an End-to-End autonomous driving ML model

— a single machine learning model that takes input from vehicle-mounted cameras and directly outputs vehicle control commands.

Autonomous driving model development is a true multi-disciplinary challenge, spanning far beyond machine learning alone. There are many areas to contribute:

data collection, dataset creation (data quality improvement, calibration, coordinate transforms), and model training (architecture design, training efficiency improvements), among others.

We are looking for engineers with a background in autonomous driving

— as well as engineers with outstanding expertise from software, robotics, or other industries.

Let's tackle one of humanity's grand challenges together.

◾️What you will work on

We work on a wide range of problems — not just model architecture improvements, but also data quality and quantity challenges.

The examples below are just a subset; if any of them resonate with your experience, we encourage you to apply.

◆Example：

・Implementation of End-to-End autonomous driving models

・Planning and strategy for data collection

・Dataset creation and improvement

-Auto-labeling model implementation and improvement

-Camera and sensor calibration

・Implementation of model training algorithms

・Optimization and speed-up of model training code

・On-vehicle model evaluation and experiment management

・Research, reproduction, and implementation of state-of-the-art papers

◾️Our development approach

We pursue both data-centric and model-centric approaches in parallel. Challenges arise from many angles — data quality issues with various root causes, architecture and backbone exploration — giving the team a wide solution space to work in. We also run large-scale training jobs on GPU clusters, so optimizing training code for speed is an active area of focus.

E2E autonomous driving is still an open problem. The model you build could become the industry standard for the next generation of self-driving systems.

【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 — not just on paper. Use feedback from the physical world to drive your development forward.

歓迎スキル

◾️Preferred qualifications

・Award or top placement in ML competitions such as Kaggle

・Contribution experience to open-source ML libraries

・Knowledge of sensor fusion and sensor calibration

求める人物像

◆Who we are looking for

・Experience with unit testing and test-driven development (TDD)

・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、OpenCV、MMDetection、ONNX、TensorRT

Middleware：Slurm

Cloud：AWS、（Databricks (DWH)）

Platform：Jetson、Linux

【Learn more】

▼Company Website

▼ Turing Tech Blog

▼Turipo (Owned Media)

▼Turing TechTalk #7 E2E Autonomous Driving Development Process

【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
• ◾️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)

■Summer vacation & 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