About The Company
DiDi's autonomous driving unit was established in 2016 with the mission of developing Level 4 autonomous driving (AD) technology to make transportation safer and more efficient. In August 2019, the unit became an independent company, DiDi Autonomous Driving, dedicated to advanced AD R&D, product application, and business expansion. We believe integrating AD technology into a shared-mobility fleet will generate immense social value. By leveraging DiDi's specialized technology, operational expertise, and integrated ecosystem, we are positioned to build and operate a highly efficient, user-oriented autonomous fleet.
About The Role
The VLA Team focuses on building large-scale foundation models for multi-agent behavior prediction and autonomous vehicle planning. By leveraging DiDi Voyager’s unparalleled driving data, we train highly robust and generalizable deep learning systems that enable safe and intelligent autonomous driving at scale.
Our models serve as the core intelligence of the autonomous driving stack, enabling vehicles to understand complex traffic scenarios, anticipate agent behavior, and make safe and efficient driving decisions.
We operate at the intersection of large-scale machine learning, autonomous driving, and foundation model research, pushing the frontier of multi-agent prediction and planning.
Responsibilities
As a member of the VLA Team, you will:
Design and train large-scale deep learning models for:
Multi-agent trajectory prediction
Behavior and intent prediction
Planning and decision-making
Build foundation model architectures (Transformers, Diffusion, Flow-based models, Decision models, VLM/VLA)
Develop scalable training pipelines across hundreds to thousands of GPUs
Work with massive real-world datasets and build high-quality data pipelines
Optimize models for latency, reliability, and on-vehicle deployment
Collaborate closely with perception, mapping, simulation, and systems teams
Drive research ideas into production systems used by real autonomous vehicles
Qualifications
Strong background in machine learning, deep learning, or robotics
Experience with PyTorch / JAX / TensorFlow
Solid understanding of modern neural architectures (transformers, diffusion, auto-regressive)
Strong coding skills in Python and C++
Passion for building real-world, safety-critical AI systems
Preferred Qualifications
BS, MS or PhD in Computer Science, Machine Learning, Robotics, or a related field
Experience in autonomous driving, robotics, or embodied AI
Experience training large models on distributed GPU clusters
Experience with trajectory prediction, planning, or decision-making systems
Publications in top ML / robotics conferences (NeurIPS, ICML, ICLR, CVPR, RSS, CoRL, etc.)
The base salary range for this position is $129,189-$247,038 annually in addition to bonus, equity and benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.
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Skills Required
- Strong background in machine learning, deep learning, or robotics
- Experience with PyTorch, JAX, or TensorFlow
- Strong coding skills in Python and C++
- Solid understanding of modern neural architectures
What We Do
DiDi Global Inc. (NYSE: DIDI) is the world’s leading mobility technology platform. It offers a wide range of app-based services across Asia-Pacific, Latin America and Africa, as well as in Central Asia and Russia, including ride hailing, taxi hailing, chauffeur, hitch and other forms of shared mobility as well as auto solutions, food delivery, intra-city freight and financial services. DiDi provides car owners, drivers and delivery partners with flexible work and income opportunities. It is committed to collaborating with policymakers, the taxi industry, the automobile industry and the communities to solve the world’s transportation, environmental and employment challenges through the use of AI technology and localized smart transportation innovations. DiDi strives to create better life experiences and greater social value, by building a safe, inclusive and sustainable transportation and local services ecosystem for cities of the future.
