EN_2010_Machine Learning Engineer（E2E Autonomous Driving Model Development）

EN_2010_Machine Learning Engineer（E2E Autonomous Driving Model Development）

仕事概要

◆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.