Machine Learning Engineer
US$174,500 – US$295,500 / yr base salary
Posted 15 Jan 2025
Advertised as “Senior Machine Learning Engineer - Foundation Model”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 3+ yrs
- Management level
- Individual contributor
- Contract
- Full-time
| Base salary | US$174,500 – US$295,500 / yr |
|---|
Field of studyComputer Engineering, Computer Science, Electrical Engineering (or related)
Study levelMaster's or higher
Field of studyComputer Engineering, Computer Science, Electrical Engineering (or related)
Study levelDoctorate
XPENG is a leading smart technology company at the forefront of innovation, integrating advanced AI and autonomous driving technologies into its vehicles, including electric vehicles (EVs), electric vertical take-off and landing (eVTOL) aircraft, and robotics. With a strong focus on intelligent mobility, XPENG is dedicated to reshaping the future of transportation through cutting-edge R&D in AI, machine learning, and smart connectivity. We are looking for a full-time Machine Learning Engineer / Research Scientist to drive the modeling and algorithmic development of XPENG’s next-generation Vision-Language-Action (VLA) Foundation Model — the core brain that powers our end-to-end autonomous driving systems. You will work closely with world-class researchers, perception and planning engineers, and infrastructure experts to design, train, and deploy large-scale multi-modal models that unify vision, language, and control. Your work will directly shape the intelligence that enables XPENG’s future L3/L4 autonomous driving products.
Key Responsibilities
- Design and implement large-scale multi-modal architectures (e.g., vision–language–action transformers) for end-to-end autonomous driving.
- Develop pretraining and fine-tuning strategies leveraging massive labeled and unlabeled fleet data (images, video, LiDAR, CAN bus, maps, human driving behaviors, etc.).
- Research and integrate cross-modal alignment (e.g., visual grounding, temporal reasoning, policy distillation, imitation and reinforcement learning) to improve model interpretability and action quality.
- Collaborate with infrastructure engineers to scale training across thousands of GPUs using distributed training frameworks (FSDP, DDP, etc.).
- Conduct systematic ablation, evaluation, and visualization of model behavior across perception, reasoning, and planning tasks.
- Contribute to model deployment optimization, including quantization, export, and latency–accuracy trade-offs for onboard execution.
Minimum Qualifications
- Master’s degree or higher in Computer Science, Electrical/Computer Engineering, or related field, with 3+ years of experience in deep learning research or productization.
- Strong proficiency in PyTorch and modern transformer-based model design.
- Experience in or (vision, language, or planning).