Okay, I've searched for GPU Performance Engineer roles in San Francisco, focusing on CUDA programming, requiring 5+ years of experience, utilizing profiling tools like Nsight, and available for full-time positions. I have found 27 positions for you.

Sure. Here's the analysis:

Job Analysis:

The GPU Performance Engineer at Genmo is fundamentally hired to optimize GPU workloads and enhance performance across the company’s infrastructure focused on video generation AI. The primary responsibilities revolve around using advanced profiling tools to identify bottlenecks and implement cutting-edge solutions that push the hardware capabilities to the limit, such as achieving unprecedented speed improvements (5-10x). Responsibilities such as writing custom CUDA kernels and collaborating with machine learning engineers highlight the interdisciplinary nature of the role, requiring a blend of deep technical expertise and teamwork. Success in this role will be characterized by the candidate's ability to drive significant performance gains, reduce latency from seconds to milliseconds, and foster a culture of optimization across teams. Challenges likely include navigating complex performance issues across multiple layers of the stack, ensuring efficient collaboration among engineers, and maintaining a keen focus on micro-level performance metrics while aligning with broader technological goals of Genmo.

Company Analysis:

Genmo operates at the cutting edge of AI video generation, positioning itself as a forward-thinking leader eager to empower the next generation of AI video creators. This innovative and research-driven atmosphere suggests a culture rooted in experimentation and technological ambition, indicating that the GPU Performance Engineer will thrive in a fast-paced, iterative environment that prioritizes openness and continuous learning. The role's fit within a research lab setting implies a high level of visibility and interaction with cross-functional teams, likely involving frequent collaboration with ML engineers and other technical specialists. Strategic alignment is evident, as this position is critical for enhancing the performance of Genmo's infrastructure, ultimately contributing to the organization's goal of developing advanced generative models. Therefore, someone in this role will be essential to achieving the company's vision, needing to embrace the challenges of cutting-edge optimization work while embodying the values of innovation and teamwork that Genmo champions.

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

• Tell me about a time when you had to optimize a system for performance. What steps did you take to identify bottlenecks, and what were the results?
• Describe a challenging technical issue you faced while developing CUDA kernels. How did you approach the problem, and what was the outcome?
• Can you provide an example of a situation where you had to work collaboratively with engineers from different teams? How did you ensure successful communication and project outcomes?
• How would you prioritize tasks when optimizing GPU workloads across different projects? What factors would influence your decision-making?
• What methodologies do you use to assess performance metrics in GPU programming, and how do you implement changes based on your findings?
• How would you approach reducing cold start latency in a serving infrastructure? What strategies would you consider, and why?
• Imagine you are faced with a critical bottleneck in GPU utilization affecting overall performance. How would you troubleshoot and resolve this issue?
• If a newly implemented model is underperforming on GPU speed, what steps would you take to diagnose and rectify the situation?
• Why are you interested in working at Genmo specifically, and how does the company’s mission resonate with your career aspirations?
• How do you see your expertise in GPU performance contributing to Genmo's goal of unlocking AGI through innovative video generation?
• What excites you most about the opportunity to push hardware to its theoretical limits, and how do you remain motivated in such high-pressure scenarios?
• Can you explain how you would evaluate the potential impact of adopting a new optimization library, such as Triton, in our existing infrastructure?
• What questions do you have for us about our current projects and how this role plays into our strategic objectives?