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Staff Planning Machine Learning Engineer

San Diego/ Santa Clara, CA
XPeng Motors is one of China’s leading smart electric vehicle (EV) companies. We design, develop, and manufacture smart EVs that are seamlessly integrated with advanced Internet, AI and autonomous driving technologies. We are committed to in-house R&D and intelligent manufacturing to create a better mobility experience for our customers. We strive to transform smart electric vehicles with technology and data, shaping the mobility experience of the future.
 
We are looking for DL engineers with strong ML/DL system design skills and software development skills. In this role, you will research, implement, and evaluate deep-learning-based methods for a mix of prediction and planning problems. You will be working with a team of the best-in-class computer vision, AI systems, and software engineers to ensure the world-leading performance on our autonomous vehicles. Your work will be supported by massive data from our autonomous fleet to deliver the best autonomous driving solution.
 
Job Responsibilities:
  • Research and develop algorithms for deep-learning-based methods for prediction and planning.
  • Design efficient model architectures that can run in real-time on the computing platform of our vehicles.
  • Develop offline data-driven ML infrastructure for fast adaptation of the planning ML models.
  • Deliver on target planning SW and closely work with the perception team to achieve the most intelligent autonomous driving systems.
  • Work with massive field-testing data to continuously improve autonomous driving technologies.
  • Designing, running, and analyzing experiments and testing to evaluate the efficiency of our solutions on real-world data.
  • Partnering with system software engineering specialists to ship industrial strength ML models.
  • Communicating and collaborating with multi-functional teams.
 
Minimum Skill Requirements:
  • MS or PhD level education in Engineering or Computer Science with a focus on Deep Learning, Artificial Intelligence, or a related field, or equivalent experience.
  • Strong experience in applied deep learning including model architecture design, model training, data mining, and data analytics.
  • 3 - 5 years + of experience working with DL frameworks such as PyTorch, Tensorflow.
  • Strong Python programming experience with software design skills.
  • Solid understanding of data structures, algorithms, code optimization and large-scale data processing.
  • Excellent problem-solving skills.
 
Preferred Skill Requirements:
  • Hands on experience in developing DL based planning engine for autonomous driving.
  • Experience in applying CNN/RNN/GNN, attention model, or time series analysis to real world problems.
  • Experience in other ML/DL applications, e.g., reinforcement learning.
  • Experience in DL model deployment and optimization tools such as ONNX and TensorRT.
 
What do we provide:
  • A fun, supportive and engaging environment
  • Opportunity to make significant impact on transportation revolution by the means of advancing autonomous driving
  • Opportunity to work on cutting edge technologies with the top talent in the field
  • Competitive compensation package
  • Snacks, lunches and fun activities
 
The base salary range for this full-time position is $180,000-$300,000, 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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