Software Engineer / Performance Optimization Engineer (Large-Scale ML Infrastructure)

TURING株式会社

この会社の求人 21件

掲載中・1日前に確認

700〜1000万円/年

※ 募集要項に記載のレンジ。実額は選考で決まります

勤務地: 東京都
休日: Weekends & public holidays
雇用形態: 正社員

どんな仕事かAIによる生成

The Performance Optimization Engineer designs, implements, and optimizes systems to maximize the efficiency of CPUs and GPUs for Turing’s end-to-end autonomous driving system. The role improves model-training efficiency, data loading and video decoding, image processing, system performance, and large-scale data-processing infrastructure.

応募資格・募集要項・仕事内容を見る

この求人に応募

給与700〜1000万円/年

募集元で応募するTURING株式会社の採用ページへ

相談する共有準備中

応募は募集元の採用ページで受け付けています。当サイトに応募フォームはありません。

応募資格

必須

Professional software development experience using Python, C++, Rust, or similar languages
Hands-on experience in performance optimization and system tuning
Experience working in a collaborative software engineering team
Business-level Japanese communication skills (equivalent to JLPT N2 or higher)
Experience in at least one of the following areas:
- Machine learning systems
- Image processing systems
- Video processing systems
- Game engine development
- Embedded software development
- High Performance Computing (HPC)

歓迎

Experience with GPU optimization using CUDA, TensorRT, or similar technologies
Experience developing multi-GPU or distributed computing systems
Experience training machine learning models using frameworks such as PyTorch

求める人物像

Individuals with the ambition to build a world-class company
Self-driven engineers who proactively take ownership of challenges
Humble team players who maintain empathy and a user-first mindset
Professionals who thrive in rapidly changing and fast-growing environments
Individuals with a strong growth mindset
Resilient problem-solvers who enjoy tackling difficult challenges

募集要項最終確認 10/16

職種: テック
雇用形態: Employment type：Full-time, permanentProbation：3 months
給与: 700〜1000万円/年
給与の詳細: ・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)・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)・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.
手当: Commuting allowanceOvertime allowance¥138,891 – ¥198,415Overtime allowance¥198,415 – ¥297,620Overtime allowance¥297,620 – ¥396,827Parking subsidy
勤務地: 〒143-0006 Tokyo Ryutsu Center Logistics Building A 6-1-1 Heiwajima, Ota-ku, Tokyo, Japan
アクセス・通勤: Commuting by public transit or personal vehicle/motorcycle is permitted.Expressway tolls and fuel costs are reimbursed.・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.)
働き方: 記載なし
勤務時間: ◾️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.
休日・休暇: Weekends & public holidays13 days paid leave granted at joining (first year)year-end/new year holidaysLife support leave: 5 days granted on day of joining (valid 1 year)Congratulatory / bereavement leave (incl. 5-day marriage leave)Maternity & parental leaveChild nursing / caregiver leave (5 days, unpaid)
福利厚生: ■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■Reduced working hours for childcare (until end of 3rd grade)
社会保険: ■Full social insurance (health, pension, employment, workers' comp)
掲載日: 2026/07/03 22:15（106日前）
最終確認: 2026/10/16 11:38（1日前）

仕事内容

About the Role

This position is intended for software engineers who are passionate about building systems that execute computationally intensive workloads faster and more efficiently.◆
At Turing, we are developing an end-to-end autonomous driving system that directly controls vehicles from onboard camera inputs.
Autonomous driving systems require numerous compute-intensive workloads—including AI inference, model training, image processing, and sensor processing—to run in real time.
As a Performance Optimization Engineer, you will be responsible for designing, implementing, and optimizing systems that maximize the efficiency of computational resources such as CPUs and GPUs.
Rather than focusing solely on algorithm development, your mission is to identify and eliminate system-wide computational bottlenecks in order to execute the same workloads faster, with lower memory consumption, and lower latency.

Responsibilities

Improve computational efficiency for autonomous driving model training
Optimize data loading and video decoding processes within model training pipelines
Develop high-performance image processing pipelines leveraging CPUs and GPUs
Tune system performance to improve memory utilization and throughput
Analyze performance bottlenecks using profiling tools
Design highly efficient systems utilizing parallel and distributed computing
Improve the performance of large-scale data processing infrastructure
Through these efforts, you will play a critical role in maximizing system scalability and performance while enabling both faster development cycles and higher model quality.
Rather than being constrained by existing frameworks, you will help design and evolve next-generation ML infrastructure from the ground up, pushing the boundaries of autonomous driving through engineering excellence.

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