Turing株式会社のEN_3012_Software Engineer (Autonomous Driving VLA Model Development)の求人

-About the role-
◆This role is open to ML engineers with expertise in machine learning, autonomous driving, or computer vision, as well as software engineers with experience in large-scale MLOps or data infrastructure development, or engineers who have worked on ML or software engineering in the robotics domain.

At Turing, we are developing an End-to-End autonomous driving model
— a system that takes input from vehicle-mounted cameras and directly controls the vehicle. Our mission is to develop a fully autonomous driving system. We are pursuing this through two main directions: training a neural network of several million parameters to imitate expert driver behavior across diverse scenarios, and running foundation models — including Vision-Language-Action (VLA) models — on the vehicle to handle a wide variety of driving situations.Applying VLA models to autonomous driving is still an emerging field even on a global scale, requiring us to explore the latest research papers and development cases while advancing the work experimentally. This means we need not only machine learning expertise, but also a wide range of engineering capabilities to drive the project forward.We are looking for members who are ready to take on these challenges. Development issues exist at many layers — from building training pipelines for foundation models to model quantization and optimization.

■What you will work on
※ You will not be responsible for all of the following. You will focus on areas where your strengths shine, while also expanding into adjacent domains.・Data calibration and coordinate transformation between different sensor devices
・Dataset creation and improvement
・Research, reproduction, and implementation of papers and existing implementations
・Evaluation of existing implementations using our proprietary datasets
・Model quantization and optimization
・On-vehicle model evaluation and experiment management
・Implementation of autonomous driving VLA models
・Auto-labeling implementation

■Enjoy being at the frontier of Embodied 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
NLP researchers from research institutions
System engineers / data scientists from system development companies
ML / software engineers from large-scale ad-tech companies

■Who we are looking for
・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
・Middleware：Slurm
・Cloud：AWS、GCP
・Platform：Jetson、Linux

【LEARN MORE】
▼Company website
https://tur.ing/

▼Turing Tech Blog
https://zenn.dev/p/turing_motors

▼Turipo（Owned media / Web Magazine）
https://tur.ing/turipo

▼Turing TechTalk #7 E2E Autonomous Driving Development Process
https://youtu.be/KHqGVkIhYp4?feature=shared

【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.

■Required qualifications
・Alignment with Turing's mission and values
・Experience building data infrastructure for ML pipelines, or extensive hands-on experience with MLOps as a software engineer
・Experience contributing to the development of complex multi-component systems by tackling bottlenecks that were blocking overall progress
・Japanese language proficiency (JLPT N2 or equivalent)

■Preferred qualifications
・Experience developing and operating distributed systems
・Data engineering experience using SQL, Spark, or similar
・Hands-on experience with IaC using AWS CDK or Terraform
・Unit testing and test-driven development (TDD) experience