j. For English Speaker の求人一覧 - Turing株式会社

◆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.

-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

-About the role- ◆This position is intended for software engineers with experience building and operating large-scale, highly reliable data platforms and distributed systems.◆ At Turing, we are developing a fully autonomous driving system that controls vehicles directly from onboard camera inputs through an end-to-end AI architecture. Improving autonomous driving AI requires collecting and managing massive amounts of data, training and evaluating models, and continuously accelerating the iteration cycle of research and development. As a Machine Learning Systems Engineer, you will build the software systems and platforms that power this entire development lifecycle. Rather than conducting machine learning research yourself, your mission is to enable ML engineers and researchers to move faster, scale further, and operate more reliably. You will design and develop a wide range of software systems that support machine learning development, including data platforms, distributed systems, developer platforms, and training/evaluation pipelines. This role goes beyond infrastructure maintenance. You will work across teams to identify bottlenecks, formulate solutions, design systems, and implement improvements that increase the productivity and effectiveness of the entire organization. ■Responsibilities ・Design, develop, and operate data platforms that support machine learning development ・Build internal services and developer platforms for ML engineers and researchers ・Design, optimize, and maintain data collection, processing, and transfer pipelines ・Automate model training and evaluation workflows ・Design, implement, and operate large-scale distributed systems ・Develop systems leveraging both cloud and on-premises infrastructure ・Improve system performance, reliability, scalability, and operational efficiency The software developed at Turing ultimately runs on real vehicles operating in the physical world. The systems you build will not only improve individual productivity but will directly influence the speed and effectiveness of our entire machine learning organization. You will collaborate with world-class ML engineers while tackling one of the most ambitious technological challenges of our time: fully autonomous driving. This position also offers opportunities to work across a broad range of technical domains—including machine learning infrastructure, data platforms, distributed systems, and developer platforms—allowing you to create significant leverage as a software engineer.

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.