Senior Machine Learning Performance Engineer

As a Senior Machine Learning Performance Engineer at XPENG, your primary responsibilities include designing and optimizing onboard software systems for deploying large deep learning models in production vehicles. You'll focus on enhancing performance, power efficiency, and minimizing latency to ensure that XPENG's autonomous driving technology operates seamlessly. Collaboration with a multifaceted team of software engineers, machine learning specialists, and research scientists is crucial, as together you will be driving innovations in AI and smart mobility.

To qualify for the Senior Machine Learning Performance Engineer role at XPENG, candidates should hold a Master’s or PhD in Computer Science, Computer Engineering, or Electrical Engineering. We're open to recent graduates as well! Proficiency in C++ and Python is essential, along with a strong grasp of computer architecture and high-performance computing. Experience in application performance analysis and optimization is preferred, and familiarity with CUDA kernels and Nvidia TensorRT will certainly set you apart.

XPENG prefers candidates with 0-3 years of industry experience, making this role particularly suitable for recent graduates. We're looking for those who have strong expertise in C++ and Python, an understanding of computer architecture, and a background in performance optimization. Experience in the autonomous driving or robotics industry, as well as skills in solving complex problems collaboratively in larger teams, are highly valued at XPENG.

At XPENG, the work environment is fun, supportive, and engaging, designed to foster collaboration and innovation. As a Senior Machine Learning Performance Engineer, you'll have access to extensive resources and infrastructures that empower you to work on the latest technologies with top talents in the field. XPENG encourages a vibrant team culture, with various activities including meals and casual outings, promoting a balanced and enjoyable workplace.

Senior Machine Learning Performance Engineers at XPENG can expect a competitive compensation package, with base salaries ranging from $174,720 to $295,680, in addition to bonuses and equity. The specific salary is determined by various factors, including the candidate’s skills, experience, and education. Employees also enjoy a range of benefits from company-provided meals to fun team-building activities, all contributing to XPENG's commitment to an attractive work-life balance.

When discussing a complex machine learning project, focus on your role in the project from conception to execution. Describe the problem, the solution you implemented, and the impact it had. Highlight your technical skills and mention the tools, methodologies, and algorithms you utilized, specifically showcasing areas that align with XPENG’s focus on performance and optimization.

To answer this question effectively, outline your process for optimizing models. Discuss techniques such as hyperparameter tuning, feature selection, and utilizing high-performance computing tools. Speak about any previous experiences you've had with CUDA or TensorRT, which are relevant to the role at XPENG, demonstrating how your strategies can enhance model performance.

Highlight any specific frameworks you have worked with, such as TensorFlow, PyTorch, or others. Discuss the types of projects you've implemented using these frameworks, emphasizing your ability to deploy deep learning models in production. Relating your experiences to XPENG’s work in autonomous driving positions you as a strong candidate for the role.

Discuss the importance of rigorous testing and validation in machine learning projects. Share your experience with techniques like k-fold cross-validation, A/B testing, or performance monitoring metrics. Explain how these practices contribute to the reliability of machine learning systems, which is vital in XPENG’s role in autonomous driving.

When asked about a tough technical challenge, focus on a specific instance that required critical thinking and problem-solving. Discuss the situation, how you approached the challenge, the solution you implemented, and what you learned from it. This showcases your resilience and technical prowess, critical traits for a Senior Machine Learning Performance Engineer at XPENG.

It's important to demonstrate familiarity with various tools for performance analysis, such as profilers, debuggers, and monitoring software. Explain how you have used these tools in the past, detailing how they helped optimize performance in prior projects, which is highly relevant to the role you're interviewing for at XPENG.

Describe your experience engaging with cross-functional teams in previous roles. Emphasize the importance of clear communication and understanding different perspectives, particularly in a collaborative environment like XPENG's. This shows you can work effectively not only with engineers but also with stakeholders from different disciplines.

Highlight your knowledge of advanced algorithms relevant to deep learning such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), or generative adversarial networks (GANs). Discuss how you've applied these algorithms in real-world applications or projects, providing insight into how they can be leveraged in autonomous driving solutions at XPENG.

Share your passion for technology and how it intersects with societal needs, particularly in improving transportation. Discuss specific aspects of autonomous driving that excite you, such as safety, efficiency, or innovation. This helps communicate your alignment with XPENG's mission to reshape the future of transportation.

To answer this question, discuss your strategies for staying current, such as attending conferences, participating in professional networks, or engaging with online courses and academic research. Mention specific publications or trends in machine learning relevant to XPENG’s focus areas that you follow closely, reinforcing your ongoing commitment to professional development.