---
title: 'Machine Learning Engineer - Agentic AI at DiDi Labs'
canonical: 'https://feeny.ai/job/machine-learning-engineer-agentic-ai-didi-labs-san-jose-nnwe3sdcm7ee'
type: 'job'
last_seen: '2026-11-11'
---

# Machine Learning Engineer - Agentic AI at DiDi Labs

- **Company:** DiDi Labs
- **Location:** San Jose, CA
- **Posted:** 2026-11-05
- **Last confirmed live:** 2026-11-11
- **Apply:** https://job-boards.greenhouse.io/didi/jobs/8356571

## Job description

## 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

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 safety gates, and reach the vehicle. The role combines machine learning, agentic systems, and autonomous driving, and will help shape both the onboard architecture and how driving software is developed.

## Responsibilities

- Build agentic harnesses for autonomous driving. Agents get permissioned access to the driving stack, data, training infrastructure, and evaluation environments, along with automatic scenario setup and pass/fail checks, so they can run experiments without a person in every step.
- Develop recursive self-improvement (RSI) loops. These include failure mining, candidate generation, evolutionary search, and targeted data generation aimed at the policy's current weaknesses. Each round should also improve the process that produces the next one.
- Curate a reusable skill library. Verified fixes become knowledge that later tasks can build on, with checks that a skill actually helps before it is promoted.
- Train LLMs on our own agent data. Turn development trajectories into training data, and fine-tune LLMs to work fluently with our software stack, tools, and codebase.
- Advance agent-as-policy approaches. Agents improve driving behavior directly: they write policy logic, curate data, adjust training recipes and objectives, and trace driving failures back to their causes.
- Shape the onboard architecture. Decide what runs on the vehicle and what stays offline, within onboard limits on latency, compute, determinism, and safety.
- Work closely with the prediction, planning, simulation, and onboard teams to turn prototypes into production workflows.

## Qualifications

- 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

## Preferred Qualifications

- Built an agentic harness or self-improving loop for a specific product or domain
- Experience in autonomous driving or robotics, especially planning, prediction, simulation, or sim-to-real transfer
- Familiarity with code-as-policy, program synthesis, LLM-driven optimization, or evolutionary search
- Publications at top ML or robotics venues (NeurIPS, ICML, ICLR, CVPR, RSS, CoRL, etc.)
- BS, MS, or PhD in Computer Science, Machine Learning, Robotics, or a related field

The annual base salary range for the Senior ML Engineer position is $169,783–$282,264. In addition to base salary, compensation includes bonus, equity, and benefits. The salary ranges are determined by role, level, and location. Individual compensation within the range depends on several factors, including work location, job-related skills, relevant experience, and education or training.

I acknowledge that prior to submitting this application, I have read and accepted the Privacy Notice for California Residents which is available on https://v.didi.cn/AQnxlBa

## About DiDi Labs

## Company Overview
- **One-liner**: DiDi Labs is the U.S. research and development arm of DiDi, focused on advancing Level 4 autonomous driving technology through large-scale AI models and real-world ride-hailing data.
- **Entity Type**: Private (subsidiary of DiDi Global Inc., which is not publicly traded)
- **Headquarters**: Mountain View, California, United States
- **Founded**: 2017 (DiDi Labs); parent company Didi Chuxing founded in 2012
- **Founders**: DiDi Labs was launched under the leadership of Cheng Wei (DiDi founder and CEO) and initially led by Dr. Fengmin Gong, Vice President of DiDi Research Institute.

## Core Business
- **Primary Industry**: Autonomous driving (Level 4), artificial intelligence, mobility technology
- **Target Customers**: B2B (integrated into DiDi’s shared-mobility fleet), enterprise partnerships (e.g., automotive OEMs, cities for smart transportation)
- **Mission**: Develop Level 4 autonomous driving technology to make transportation safer and more efficient, and integrate it into a shared-mobility fleet to generate broad social value.

## Products & Services
- **VLA (Vision-Language-Action) Foundation Models**: Large-scale deep learning systems for multi-agent behavior prediction and autonomous vehicle planning, built on DiDi’s massive real-world driving dataset.
- **Behavioral & Motion Planning Systems**: Core decision-making and trajectory optimization algorithms that enable safe, smooth navigation in complex urban environments.
- **AI Research (Computer Vision, NLP, Speech, Mapping)**: Through DiDi AI Labs (China-based but collaborative), the company develops speech interaction, smart mapping, and perception solutions.
- **Autonomous Vehicle Testing & Fleet Operations**: DiDi Autonomous Driving operates test fleets in the United States and China, and runs open-source challenges (e.g., Udacity Self-Driving Car Challenge).

## Market Standing
- **Valuation**: Not publicly disclosed for DiDi Labs; parent DiDi Global was valued at over $60 billion during its 2021 IPO (now delisted and private).
- **Key Metric**: Total funding for DiDi exceeds $20 billion across multiple rounds (SoftBank, Tencent, etc.). DiDi Labs benefits from parent’s resources.
- **Notable Investors/Partners**: DiDi’s investors include SoftBank Vision Fund, Tencent, Uber, Apple, and others. DiDi Labs partners with Udacity, and its team includes security expert Charlie Miller.
- **Growth Signals**: Independent autonomous driving unit established in 2019; active hiring in 2025 for VLA and motion planning roles; integration with DiDi’s massive ride-hailing network (over 500 million users) provides unmatched real-world data.

## Competitive Advantages
- **Massive Real-World Data**: Access to DiDi’s billions of trips across Asia, Latin America, and Australia gives the team proprietary training data for behavior prediction and planning.
- **Integration with Shared Mobility**: Autonomous fleet can be deployed directly into an existing ride-hailing platform, offering a clear go-to-market path.
- **Top Talent & Research**: Deep roster of AI researchers, published in top conferences (NeurIPS, CVPR, ICLR, etc.), and strong collaboration with global research network.
- **Open Innovation**: Hosts open-source challenges and engages with the broader self-driving community.

## Strategic Focus
- **Current Priorities**: Scaling VLA foundation models for multi-agent prediction and planning; deploying Level 4 autonomous vehicles in operational domains; optimizing latency and safety for production deployment.
- **Direction for Growth**: Expanding the autonomous fleet, deepening partnerships with cities, and building the “smart transportation ecosystem” through AI-driven infrastructure.

## Why Work Here
- **Cutting-Edge AI at Scale**: Work on large-scale distributed training (hundreds to thousands of GPUs), transformers, diffusion models, and real-time planning for safety-critical systems.
- **Impact**: Directly contribute to autonomous driving technology that could transform urban mobility and reduce traffic accidents.
- **Compensation & Benefits**: Competitive base salary (e.g., $129k–$247k for ML Engineer, plus bonus, equity, and benefits). Roles in Mountain View with hybrid/office expectations.
- **Culture & Perks**: Collaboration with world-class researchers, access to massive datasets, and a mission-driven environment focused on safety and innovation.
- **Work Model**: Based in Mountain View, CA; roles are likely on-site or hybrid given the hands-on nature of autonomous vehicle development.

## Sources
1. [Job Board - Machine Learning Engineer, VLA](https://job-boards.greenhouse.io/didi/jobs/7975368)
2. [Job Board - Software Engineer, Motion & Behavioral Planning](https://job-boards.greenhouse.io/didi/jobs/7975371)
3. [PRNewswire - DiDi Launches U.S. Labs in Silicon Valley](https://www.prnewswire.com/news-releases/didi-launches-us-labs-in-silicon-valley-to-build-global-nexus-of-innovation-300420435.html)
4. [Golden - DiDi Labs Company Profile](https://golden.com/wiki/DiDi_Labs-GE84KD8)
5. [DiDi Official - AI Labs Page](https://www.didiglobal.com/science/ailabs)

## Other roles at DiDi Labs

- [Sr. / Staff Software Engineer, Infrastructure (Autonomy)](https://feeny.ai/job/sr-staff-software-engineer-infrastructure-autonomy-didi-labs-san-jose-46z14fjyjr4e) — San Jose, CA
- [Staff/Principal AI Transformation Engineer](https://feeny.ai/job/staff-principal-ai-transformation-engineer-didi-labs-san-jose-0q3b0yenvgj4) — San Jose, CA
- [Research Intern, Scene Understanding and Planning](https://feeny.ai/job/research-intern-scene-understanding-and-planning-didi-labs-san-jose-re06bc12hdvy) — San Jose, CA
- [ML Engineer, Motion Planning](https://feeny.ai/job/ml-engineer-motion-planning-didi-labs-san-jose-03dysefnhh2g) — San Jose, CA
- [Sr. /Staff System Safety Engineer, Autonomy](https://feeny.ai/job/sr-staff-system-safety-engineer-autonomy-didi-labs-san-jose-4n27gkmr8zry) — San Jose, CA
- [Software Engineer, Motion Planning](https://feeny.ai/job/software-engineer-motion-planning-didi-labs-san-jose-9p04hs5ps8a0) — San Jose, CA
- [Motion Planning Engineer (PhD, Intern)](https://feeny.ai/job/motion-planning-engineer-phd-intern-didi-labs-san-jose-ggrryq4gd28h) — San Jose, CA
- [Senior/Sr. Staff AI Infrastructure Engineer, Inference & Optimization](https://feeny.ai/job/senior-sr-staff-ai-infrastructure-engineer-inference-optimization-didi-labs-san-zg861r8xhm7c) — San Jose, CA
- [Software Engineer / Sr. Software Engineer, Planning Selection Autonomy](https://feeny.ai/job/software-engineer-sr-software-engineer-planning-selection-autonomy-didi-labs-qp2kzcd84107) — San Jose, CA