Tesla Autopilot Engineer Interview: Perception, Planning, and Control Full-Chain Assessment

Background

Let me start with my background: I graduated with a Master's from a top Chinese university and spent two years at an autonomous driving startup, mainly working on perception algorithms—dealing with point clouds and images on a daily basis. Earlier this year, I started looking for new opportunities, and Tesla was at the top of my list. After all, they're the pioneers in production autonomous driving, and their FSD system is genuinely impressive.

I applied through a referral from a former colleague, and got a call from HR about three days later to schedule the interviews. The entire process took about three weeks: three technical rounds plus an HR round, with a fairly tight schedule. Let me walk through each round in detail.

Interview Process Review

Round 1: CV Perception + Object Detection (about 1.5 hours)

The first interviewer was a young-looking guy who turned out to be the tech lead of the perception team. He started with a brief self-introduction and then dove straight into technical questions.

He asked about the detection algorithms we use in our current project. I explained our BEV-based 3D object detection using BEVFormer. He followed up on BEVFormer's core approach—I walked through the spatial attention mechanism, how 2D features are lifted into 3D BEV space, and how temporal fusion works. He nodded and then hit me with a tough question: BEVFormer's detection accuracy drops significantly for distant targets—have you made any improvements?

I had actually dealt with this issue before. We used multi-scale feature fusion to alleviate the sparsity of long-range features and added a distance-aware loss weight. I explained these details, and the interviewer clearly got more interested, asking about the specific formula for the loss design.

Next was the coding section. The problem: Given a set of 2D bounding boxes and corresponding camera extrinsic/intrinsic matrices, implement a simple frustum-based pseudo-3D detection. This wasn't overly difficult, but there were many details to handle, especially around coordinate transformations where it's easy to make mistakes. I finished in about 25 minutes. The interviewer said the logic was correct but asked me to consider edge cases—like when a box extends beyond the image boundary.

We spent the last 15 minutes discussing project details, mainly about the data annotation pipeline and model deployment. Overall, Round 1 went well—the interviewer was professional and the questions were on point.

Round 2: Planning Algorithms + Decision Trees (about 1.5 hours)

The second interviewer was a senior engineer who immediately drew a scenario on the whiteboard: On a highway, the ego vehicle is in the middle lane. There's a slow car 100 meters ahead, a vehicle in the left lane is changing lanes toward you, and the right lane is empty. How does the planning module handle this?

This is a classic scenario. I started from scene understanding, discussed behavior prediction for surrounding vehicles' trajectories, then how the decision-making layer uses decision trees or POMDP for decisions, and finally how motion planning generates specific trajectories. The interviewer followed up: If the left vehicle suddenly accelerates and completes the lane change, how does your decision tree handle this dynamic change?

I explained that this requires re-evaluation at every planning cycle, that the leaf nodes of the decision tree need to account for temporal changes, and that a rolling horizon approach can be used. He then asked: How do you determine the horizon length for rolling horizon? What are the problems if it's too short or too long? I had thought about this before—too short leads to myopic planning, too long increases computation and makes predictions less accurate. Generally, 3-5 seconds is a reasonable range.

The coding problem was: Implement a simple lattice planner that, given a start and end point, generates a set of candidate trajectories and ranks them using a cost function. I had implemented something similar before, so I wrote it fairly quickly. But the interviewer asked me to explain the cost function design in detail—how the weights for comfort, safety, and efficiency are tuned. We spent a long time on this part.

Round 2 felt deeper than Round 1. The interviewer kept pushing until I couldn't answer anymore, but it never felt like he was being difficult—it felt more like a technical discussion.

Round 3: Deep Dive into Projects + System Design (about 2 hours)

Round 3 was with the department director. He had a strong presence but spoke gently. He asked me to walk through a complete project I had worked on, and I chose our multi-modal fusion perception project. He kept interrupting with questions:

Is your fusion strategy early fusion or late fusion? Why did you choose that approach?

How do you handle time synchronization between LiDAR and cameras? What's the error level?

If a sensor suddenly fails, how does the system achieve graceful degradation?

These questions got progressively deeper. For the sensor failure question, I had only considered simple fallback strategies before. He pushed me to think from a system architecture perspective—including how to implement sensor health monitoring and how to use uncertainty estimation at the perception level to guide downstream decision-making.

The system design question was: Design a full-chain perception-planning-control system for urban NOA. What modules are needed? How do they communicate? What are the latency requirements?

This was a big question. I started by drawing a system architecture diagram, explaining the perception module's output format and frequency, the planning module's inputs and outputs, the control module's execution frequency, and the message passing mechanism in between. He was particularly focused on latency—What's the end-to-end latency requirement from perception to control? How do you ensure it? I said perception takes about 50ms, planning 30ms, control 10ms, totaling around 90ms, achieved through pipeline parallelism and GPU acceleration.

After Round 3, I was completely drained, but I also felt like I learned a lot. The interviewer's questions really made me reconsider many aspects I hadn't thought deeply about before.

Key Interview Questions

1. What is BEVFormer's core approach? How is spatial attention implemented?

2. Causes and solutions for declining detection accuracy at long range?

3. Given 2D bboxes and camera parameters, implement frustum-based pseudo-3D detection

4. How does the planning module handle dynamic changes of surrounding vehicles in highway scenarios?

5. How to determine the horizon length for rolling horizon?

6. Implement a lattice planner and design a cost function

7. Pros and cons of early fusion vs late fusion?

8. Multi-sensor time synchronization approaches?

9. Graceful degradation strategies when sensors fail?

10. Full-chain system design for urban NOA: module division, communication mechanisms, latency requirements

Lessons and Advice

First, fundamentals must be solid. The interview won't test obscure tricks, but you must truly understand fundamental concepts. For example, with BEV's spatial attention, you need to be able to explain everything from mathematical principles to engineering implementation without gaps.

Second, be able to discuss your project experience in depth. The interviewers care deeply about whether you truly understand the projects you've worked on, not just that you ran a model. You need to explain the reasoning behind every technical choice, the pitfalls you encountered, and the improvements you made.

Third, systems thinking is crucial. Especially in Round 3, the interviewer was focused on your understanding of the entire system, not just individual modules. Autonomous driving is a systems engineering challenge—perception, planning, and control are tightly coupled. You need to be able to see problems from a global perspective.

Fourth, coding skills matter. While the coding questions aren't as difficult as pure algorithm interviews, they're closely tied to the business domain, and you're expected to explain your thinking as you code. Practice is essential.

Fifth, stay calm and honest. If you don't know something, say so. The interviewers value your thought process more than correct answers. I didn't answer a few questions perfectly, but I shared my reasoning and guesses, and still got positive feedback.

FAQ

Q: What's the workload like in the autonomous driving team?

A: From what I learned during the interview, overtime is common, especially during project crunch periods. But compared to some startups, the pace is manageable—weekends are generally free.

Q: Is the interview in Chinese or English?

A: Entirely in Chinese. Using English for technical terms is fine; no English interview is required.

Q: What are the education requirements?

A: Perception algorithm roles generally require a Master's degree or above. Planning and control roles may accept Bachelor's degrees if you have solid project experience.

Q: How long does it take to get interview results?

A: In my case, Round 2 was scheduled 3 days after Round 1, Round 3 was 5 days after Round 2, and the final result came one week after Round 3. The whole process took about three weeks.

Q: How's the compensation?

A: With 2 years of experience, the total package is roughly in the 50-70w RMB range, depending on interview performance and level. Stock compensation makes up a significant portion but has a long vesting period.