DiDi Global is the world's leading mobile transportation platform offering a full range of app-based services to users around the world. Backed by Techstars.
About the role
Autonomous driving software today improves mostly by hand: engineers collect data, train models, and tune modules. This role explores a different approach, in which AI agents take part in that work. Agents propose changes to policy code, data, training recipes, or model code. They then evaluate the results, diagnose failures, and turn verified fixes into reusable skills and data for the next round. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Strong background in machine learning, such as deep learning, reinforcement learning, or decision-making systems
- Hands-on experience building LLM agent systems, such as tool use, multi-agent orchestration, or automated evaluation loops
- Experience training or fine-tuning LLMs for agentic workflows, including trajectory data curation, supervised fine-tuning, or RL from execution feedback
- Strong Python skills and working C++
- Rigorous experimental habits: controlled comparisons, held-out evaluation, and a clear line between what was measured and what was assumed
- Ability to own a system from research prototype to production
More about this role
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.
Autonomous driving software today improves mostly by hand: engineers collect data, train models, and tune modules. This role explores a different approach, in which AI agents take part in that work. Agents propose changes to policy code, data, training recipes, or model code. They then evaluate the results, diagnose failures, and turn verified fixes into reusable skills and data for the next round. With DiDi Voyager's real-world driving data and large-scale evaluation infrastructure, the goal is a loop that produces improvements that generalize across scenarios, pass...
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