EN_3012_Software Engineer / Performance Optimization Engineer (Large-Scale ML Infrastructure)
- Compensation
- Not disclosed
- Location
- ■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.)
- Work setup
- Not specified
- Seniority
- Principal
- Employment
- Full time
- Experience
- 1+ years
- Category
- Software engineering
- Posted
Skills
Full description
EN_3012_Software Engineer / Performance Optimization Engineer (Large-Scale ML Infrastructure)
仕事概要
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.
必須スキル
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.