Member of technical staff (Inference)

As a Member of Technical Staff (Inference) at H, you will be responsible for developing scalable, low-latency inference pipelines that enhance the performance of our AI models. Your role will involve optimizing model performance by focusing on memory usage, throughput, and latency through techniques like distributed computing and model compression. Additionally, you will create specialized GPU kernels for tasks that demand high performance and collaborate with research teams to implement state-of-the-art inference techniques.

To be considered for the Member of Technical Staff (Inference) position at H, you should hold a Master's or PhD in Computer Science, Machine Learning, or a related field. Proficiency in programming languages such as Python, Rust, or C/C++ is essential, along with experience in GPU programming with tools like CUDA and Open AI Triton. Familiarity with model compression and quantization techniques will give you an edge, as well as a collaborative mindset and strong communication skills.

Yes, the position of Member of Technical Staff (Inference) at H offers flexibility for remote or hybrid work arrangements. While our teams are based in France, the UK, and the US, this role can be fully remote or hybrid if you're located in cities where we have offices, such as Paris or London. The final decision regarding the work arrangement will be made by the hiring manager.

At H, the culture is centered around openness, learning, and collaboration. As a Member of Technical Staff (Inference), you'll be joining a fun and dynamic multidisciplinary team that values every contribution. We believe that each team member has something unique to offer, leading to a culture of continuous learning, exploration of new challenges, and innovative thinking.

H is dedicated to supporting professional growth and career development for our team members. As a Member of Technical Staff (Inference), you'll find ample opportunities for continuous learning through collaboration with world-class AI talent. We encourage exploring various challenges and provide resources for you to enhance your skills and advance your career in the rapidly evolving field of AI.

When answering this question, highlight your understanding of different optimization techniques such as model compression, quantization, and using distributed computing. Discuss how you might analyze existing models for performance bottlenecks and how you would implement solutions to improve memory usage, throughput, and latency effectively.

In your response, explain that GPU programming utilizes graphics processing units to handle complex computations required for inference in AI models. It's important because GPUs can significantly accelerate the processing time for tasks like matrix multiplications and model evaluations compared to traditional CPU processing. Reference CUDA as a key framework for this.

Share specific experiences regarding your work with optimization techniques such as model compression and quantization. Provide examples where you successfully implemented these techniques to reduce model size or increase throughput, highlighting the impact of these changes on application performance.

Illustrate a specific scenario where you worked with a multidisciplinary team to tackle a significant technical challenge. Focus on your role in brainstorming solutions and how your contributions, along with your team’s collaboration, led to a successful and effective resolution of the problem.

Discuss various methods you use to stay informed, such as reading industry publications, following relevant blogs, attending conferences, or participating in online forums. Mention any specific papers or conferences that have inspired your work in AI to reflect your commitment to continual learning.

Provide insights into your hands-on experience with popular deep learning frameworks. Describe projects where you've implemented these frameworks to develop or optimize inference processes, emphasizing any performance improvements achieved and your understanding of their functionalities.

Choose a compelling example where you applied a recent advanced technique in AI. Detail your thought process and the outcomes of using this technique, illustrating how it improved the project's results and contributed to achieving its goals.

Share your systematic approach to debugging by emphasizing the importance of understanding the entire pipeline's architecture. Discuss tools or methods you use for monitoring performance and identifying bottlenecks, then outline how you collaboratively resolve issues that arise.

Mention your proficiency in programming languages relevant to the role, such as Python, Rust, or C/C++. Describe specific projects where you've effectively utilized these languages to achieve project goals, illustrating your versatility and application of coding skills in AI developments.

Explain that you value open communication, knowledge sharing, and a willingness to learn from others. Discuss how your collaborative mindset has led to innovative solutions and strengthened team dynamics in past projects, emphasizing the importance of working together in complex technical settings.