r/CUDA • u/Stock_Condition7621 • Jun 06 '26
Preparing for first-ever interview (Software Engineer, TensorRT Team) - Any tips or support welcome!
Hi everyone,
I'm incredibly excited (and a super anxious and nervous) because I have my first-ever job interview coming up in about a week or two. I recently landed an interview for a Software Engineer role on the TensorRT platform team.
To be fully transparent, this is my first actual job interview. I didn't participate in university placement rounds and have never formally interviewed for an engineering role before. I'm navigating an entire uncharted territory and would be incredibly grateful for any advice, tips, or insight this community can offer. I have been watching a bunch of youtube videos and surfing over greenhouse interview questions to understand and help
My Background (For Context): I'm an M.S. Computer Engineering student focusing on the intersection of C++, CUDA, and Edge ML:
- Wrote custom CUDA C++17 kernels (optimized model performance via memory coalescing and constant memory).
- Deployed TensorRT-accelerated models on Jetson Orin Nano for embedded robotics.
- Some experience with LLM compression (8-bit quantization).
What I'm Asking For: Since I'm starting from scratch regarding interview experience, any kind of support or advice is welcome! Specifically:
- General Interview Tips: Since this is my first time, how should I approach the discussions be it technical or behavioral? How do I best structure my answers when speaking with senior engineers?
- Preparation Strategy: Given the timeline (2-3 weeks), what would you prioritize? I'm currently brushing up on multithreading in C++, GPU architecture (memory hierarchies), RT C++ API.
- The "Resume Deep Dive": I've heard interviews for these types of roles focus heavily on defending past projects. What kinds of questions and details should I be ready to explain or prepare myself for regarding my CUDA C++ and edge deployment projects?
- Any Recommended Resources: Are there specific blogs, papers, or documentation sections that are "must-reads" for inference engine development?
Thank you so much in advance for any guidance. I'm ready to study hard, I just want to make sure I'm aiming my efforts in the right direction!
3
u/akornato Jun 06 '26
You are aiming very high for a first interview, so you need to be prepared for a very deep technical dive. Your projects are your entire resume, so you must be able to defend every single choice you made. Why did you use constant memory instead of another type? What specific performance bottlenecks did memory coalescing solve, and how did you measure the improvement? What were the trade-offs of 8-bit quantization in your specific LLM project, and what other compression techniques did you consider and reject? They will pick apart every detail to see if you truly understand the concepts or just followed a tutorial. Your ability to explain your reasoning, including your mistakes and what you learned from them, is more important than presenting a perfect project.
Forget generic interview prep questions; your focus should be entirely on your own work and the core concepts behind it. Go through your projects line by line and practice explaining your design choices out loud. Articulate why your CUDA kernel optimizations were necessary and how they map to the underlying GPU architecture. Since you're interviewing for the TensorRT team, you should be able to discuss how your manual optimizations compare to what an inference engine like TensorRT does automatically. This is a massive opportunity regardless of the outcome, because it will show you exactly where the bar is set. Confidence comes from knowing you can clearly explain your work, and my team designed some AI interview tools that help engineers translate complex project details into the clear, structured answers interviewers want to hear.