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Senior Machine Learning Engineer - AI Foundation

XPENGSanta Clara🇺🇸United States

Why this role stands out

This hybrid role offers a significant opportunity to shape the future of AI foundations at XPENG, a leader in smart mobility. You'll thrive here if you are passionate about cutting-edge ML research and development and eager to contribute to groundbreaking advancements in autonomous driving and intelligent transportation. Apply now to join a dynamic team and make a real impact.

Quick overview

Salary$174.7k - $295.7k/yr
SeniorityMid Senior
Work modeHybrid
LocationSanta Clara, United States
Edge ComputingMachine LearningRoboticsCUDAData PipelineAutonomous DrivingLLMPyTorch

Job description

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 - AI Foundation, with deep knowledge and strong enthusiasm towards establishing a state-of-art ML infrastructure for training very large foundation model and accelerating model training/inference.
Our mission is to solve the autonomous driving problem. You will work with a team of talented software engineers, machine learning engineers and research scientists to push the boundary of state-of-art machine learning models which will enable the next-generation E2E solution of autonomous driving.
Job Responsibilities:
  • Design and implement training data pipeline that streams data from hundreds of petabytes of labeled and unlabeled data from a fleet of over a million vehicles.
  • Implement training framework for all physical AI foundation models in XPeng, including VLA 2.0, XWorld, Robotics.
  • Accelerate training with state of the art parallelisms, e.g., FSDP, Expert Parallel, Context Parallel, and data types.
  • Accelerate model inference on the cloud for closed-loop simulation, reinforcement learning, and enterprise LLM/VLM applications.
Minimum Skill Requirements:
  • Master's Degree in CS/CE/EE, or equivalent, in industry experience.
  • Deep knowledge of PyTorch.
  • Knowledge of model inference framework (e.g. vLLM, SGLang)
  • In-depth knowledge of transformer architecture and ways to accelerate the training and inference of transformer models.
  • Experience of performing large scale distributed training of models.
  • A track record of profiling model and doing detective work to improve model training and inference speed.
Preferred Skill Requirements:
  • Previous experience in the autonomous driving industry.
  • Experience with CUDA language for writing custom ops.
  • Experience with edge computing systems.
  • Knowledge of disributed computing frameworks, such as Ray.
  • A track record of efficiently solving complex problems collaboratively on larger teams
What do we provide:
  • A fun, supportive and engaging environment.
  • Infrastructures and computational resources to support your work.
  • Opportunity to work on cutting edge technologies with the top talents in the field.
  • Opportunity to make a significant impact on the transportation revolution by the means of advancing autonomous driving.
  • Competitive compensation package.
  • Snacks, lunches, dinners, and fun activities.
The base salary range for this full-time position is $174,720-$295,680, in addition to bonus, equity and benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.
We are an Equal Opportunity Employer. It is our policy to provide equal employment opportunities to all qualified persons without regard to race, age, color, sex, sexual orientation, religion, national origin, disability, veteran status or marital status or any other prescribed category set forth in federal or state regulations.

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