I just finished the full interview for the MLE position at Tesla US, and it was extremely intense. I was on edge the entire time, and when I walked out of the office, I realized I was covered in sweat… The pressure was real.
1️⃣ Phone Screen
During the self-introduction, I talked about the ML programs I had participated in before. After I finished, the interviewer also asked why I wanted to join Tesla. They really value mission alignment—when preparing, you must first learn about the company’s culture and long-term vision.
I was also asked about my understanding of Autopilot technology, which requires a certain depth of knowledge in the autonomous driving field. I answered from the perspectives of Autopilot’s functions, technical architecture, and its role in improving driving safety and convenience.
For the technical part, they tested the application of the computer vision pipeline in autonomous driving, as well as questions like how to handle latency in real-time inference.
2️⃣ Virtual Onsite
There are three rounds, each with a different focus:
Round 1
I was asked to derive backpropagation, explain solutions to gradient-related problems, and discuss the differences between batch normalization and layer normalization, among other foundational ML topics. This round heavily emphasized theoretical depth and mathematical understanding.
Round 2
This round focused on domain-specific questions related to autonomous driving scenarios, including system design, sensor fusion strategies, and model safety verification. Expect interviewers to challenge your assumptions and dig deeper into how your designs perform under real-world constraints.
Round 3
The final round centered on infrastructure and scalability. For example, I was tested on how to use distributed computing frameworks like Apache Spark and TensorFlow Distributed to improve training efficiency.
When discussing continuous learning after model deployment, I introduced technologies such as incremental learning and online learning, as well as how feedback mechanisms can be leveraged to optimize models in real time. Finally, there was a live coding session that strongly tested practical engineering ability and code clarity.
✅ Final Takeaways
Overall, the MLE interview process is extremely comprehensive. If you’re targeting this role, preparation cannot be superficial—you need strong fundamentals in Machine Learning, a solid understanding of autonomous driving technologies, and hands-on programming skills.
Just as importantly, be ready for deep follow-up questions. Interviewers are not only evaluating what you know, but how you think, design systems, and handle production-level challenges.
If you’re also aiming for MLE, DS, or AI roles at Tesla or other top North American tech companies but aren’t sure how to structure your projects, refine your interview responses, close knowledge gaps in ML and autonomous driving, or simulate real interview pressure, consider getting expert guidance.
Programhelp specializes in North American MLE/DS/AI job preparation, supporting candidates with resume optimization, project refinement, technical interviews, system design, and live coding. With targeted preparation aligned to top-tier hiring standards, you can avoid unnecessary detours and move more efficiently toward landing your dream offer.
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