Okay, I've searched for full-time Machine Learning Engineer positions focused on large language models and AI in Santa Clara, CA, requiring a Ph.D. in Computer Science and 5+ years of experience. I have found 47 positions for you.

Sure. Here's the analysis:

Job Analysis:

This role as a Machine Learning Engineer focused on Large Language Models (LLMs), AI, and Robotics at XPENG is fundamentally designed to drive cutting-edge innovation in autonomous systems by developing, fine-tuning, and deploying sophisticated language models tailored for humanoid robots and autonomous driving applications. The candidate will need to navigate complex technical challenges such as model efficiency (quantization, weight sharing), scalability of training and deployment pipelines, and leveraging massive, diverse datasets, including synthetic data generation. Success in this role means not only delivering high-performance models suited for constrained, real-world environments such as vehicles and robots, but also effectively collaborating across multidisciplinary teams to align AI advancements with product requirements. The necessity of a Ph.D. underscores that this role demands deep theoretical knowledge alongside strong software engineering skills and hands-on experience with the entire lifecycle of modern LLMs. Soft skills such as openness and communication play a critical role given the experimental nature of this emerging tech domain and cross-functional dependencies. Autonomous decision-making will be frequent, especially in choosing techniques to optimize or adapt models for specific robotic or vehicular contexts, as well as in identifying novel data strategies to enhance model quality. Within 6-12 months, an impactful candidate should have contributed to scalable training pipelines, improved locally deployable LLMs, and demonstrated the ability to integrate AI innovations seamlessly into XPENG’s intelligent mobility products.

Company Analysis:

XPENG operates as a forward-thinking technology leader at the intersection of intelligent mobility, electric vehicles, and robotics, with a strong focus on research-driven innovation. The company’s emphasis on AI and autonomous driving technologies positions it as a disruptor in both the automotive and emerging eVTOL markets, supported by heavy investment in R&D and a global presence spanning China, Europe, and the U.S. This environment nurtures a culture that values technical excellence, collaboration, and agility, where employees are empowered to pioneer new solutions with significant real-world impact. For a Machine Learning Engineer in Santa Clara, this means working in a highly multidisciplinary, innovation-focused culture that balances ambitious product goals with the scientific rigor necessary for advanced AI development. The role fits squarely within XPENG’s core strategic efforts to build intelligent operating systems and autonomous driving capabilities, suggesting high visibility and meaningful influence on the company’s trajectory. Candidates should expect a dynamic setting combining startup-style innovation energy with the resources of a well-funded global tech company. Success here requires not merely technical mastery but also the curiosity and resilience to push boundaries in a fast-evolving industry.

Absolutely. Here are some mock interview questions that could come up:

• Behavioral: Tell me about a time when you had to fine-tune a pre-trained model for a very specific use case. How did you approach it and what were the outcomes?
• Behavioral: Describe a situation where you had to coordinate with multiple teams to align AI development with product objectives. How did you ensure effective communication and collaboration?
• Behavioral: Give an example of when you had to adapt to ambiguity or changing requirements during a research or engineering project. How did you handle it?
• Behavioral: Can you share an experience where you proposed or implemented a novel technique to improve model efficiency or data quality?
• Technical: How would you design and implement an efficient LLM training pipeline that can scale to massive datasets while also enabling rapid iteration?
• Technical: Discuss your experience with model quantization or weight sharing techniques. What challenges did you face and how did you overcome them?
• Technical: Explain how you would approach generating synthetic data to improve the diversity and quality of training data for an LLM targeted at robotic applications.
• Technical: What are key considerations when deploying multi-modal LLMs locally in resource-constrained environments such as autonomous vehicles or humanoid robots?
• Situational: Imagine you find that the fine-tuned LLM deployed in an autonomous vehicle is underperforming due to unexpected edge-case inputs. How would you diagnose and address this problem?
• Situational: If you identify a significant misalignment between AI model capabilities and product team expectations midway through a development cycle, how would you rectify this?
• Situational: Suppose your research suggests a promising new method for weight sharing that could improve deployment efficiency, but it conflicts with current engineering standards. How would you proceed?
• Company Fit: What about XPENG’s mission and approach to intelligent mobility excites you the most, and how do you see yourself contributing to that vision?
• Company Fit: XPENG emphasizes a collaborative and open work environment. How have you demonstrated these qualities in your previous roles?
• Company Fit: How do XPENG’s global and multi-disciplinary R&D efforts align with your career goals and working style?
• Questions for Interviewer: Can you describe the typical cross-functional teams I would work with and how collaboration is facilitated at XPENG?
• Questions for Interviewer: What are the biggest technical challenges the LLM and AI teams currently face in deploying models at scale in autonomous products?
• Questions for Interviewer: How does XPENG support continuous learning and innovation for employees working in such rapidly evolving AI technologies?