Software Engineer (SoC Optimization)

TURING株式会社

この会社の求人 22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

下限は東京都の機械学習エンジニアの求人の中央値（600万円）より100万円高い

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

This role focuses on optimizing edge AI inference and deployment on automotive hardware, including improving inference performance, automating model deployment workflows, and enabling new SoC platforms. The position works across ML models, compilers, runtimes, and hardware for autonomous driving applications.

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

Hands-on experience implementing inference pipelines using SDKs such as NVIDIA TensorRT or similar inference acceleration frameworks.
Experience analyzing and optimizing machine learning inference bottlenecks using profiling tools such as NVIDIA Nsight.
Experience implementing custom operators and supporting PyTorch ↔ ONNX model conversion workflows.
Knowledge of and practical experience with model optimization techniques such as quantization, pruning, or other inference acceleration methods.
Experience with parallel programming using CUDA or equivalent GPU programming frameworks.

歓迎

Experience developing systems involving sensor fusion, LiDAR, SLAM, computer vision, or camera-based perception.
Hands-on experience deploying and optimizing software on automotive SoCs, such as NVIDIA DRIVE Orin or similar autonomous driving platforms.
Professional experience in the autonomous driving and/or robotics domains.
A proven track record of contributions to open-source software (OSS) projects.

求める人物像

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

使用ツール

Python OpenCVMMDetectionSlurmAWS GCPJetsonLinux

募集要項最終確認 10/26

職種: 機械学習エンジニア
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 allowanceSubsidized lunchInfluenza vaccination subsidyParking subsidy
勤務地: 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.・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 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■Child nursing / caregiver leave (5 days, unpaid)
福利厚生: ■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
社会保険: ■Full social insurance (health, pension, employment, workers' comp)
選考の流れ: · 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.
掲載日: 2026/09/11 03:10（46日前）
最終確認: 2026/10/26 09:53（1日前）

仕事内容

-About the role-

This position is intended for software engineers with experience in edge AI inference optimization and deployment optimization.
In this role, you will work across the boundaries of ML models, compilers, runtimes, and SoC/hardware, taking ownership of enabling stable and high-performance inference in resource-constrained automotive edge environments. Your mission is to identify the optimal balance between performance and cost for production-scale deployment, while ensuring reliability and maintainability, and translate those trade-offs into concrete system performance requirements.

1. Accelerate Inference Performance

Collaborate across ML and systems teams to optimize end-to-end inference performance, from identifying bottlenecks to implementing improvements.
Improve execution efficiency of ML models running on automotive edge devices.

2. Improve the Deployment Pipeline

Automate the entire model deployment workflow, including model conversion, quantization, build, and deployment.
Build and maintain automated build environments and CI pipelines for reliable deployment.

3. Enable New SoC Platforms

Evaluate and bring up next-generation SoCs through exploratory development.
Drive model deployment, implementation, and performance optimization on new hardware platforms.