HW/SW Co-Design Engineer - job 1 of 4

As a HW/SW Co-Design Engineer at OpenAI, you'll work closely with hardware vendors to co-design future hardware that optimizes programmability and performance. You'll engage with internal teams to understand their machine learning needs and ensure that our hardware solutions support those requirements. Your role will involve developing performance estimates, managing communications with partners, and influencing hardware roadmaps to enhance AI workloads.

To excel as a HW/SW Co-Design Engineer at OpenAI, you should have a minimum of 4 years of industry experience, preferably with a background in hardware and software co-design. Strong knowledge of GPU architecture and experience with accelerator programming languages like CUDA are essential. Advanced education such as a PhD in a relevant field is a plus, but practical experience will shine just as brightly!

At OpenAI, we prioritize employee well-being for all positions, including HW/SW Co-Design Engineers. We offer comprehensive medical, dental, and vision coverage for you and your family, mental health support, and a generous 401(k) matching plan. Additionally, we provide unlimited time off, ample holidays, and a yearly stipend for learning and development to ensure you feel valued and motivated.

The team culture at OpenAI is collaborative and innovative. As a HW/SW Co-Design Engineer, you'll be part of a diverse group of passionate professionals who are eager to push the boundaries of AI technology. We encourage open communication and active collaboration across teams, fostering an environment where your voice and ideas can make a real difference in our projects.

At OpenAI, HW/SW Co-Design Engineers work with advanced technology stacks that include GPU architectures, CUDA for accelerator programming, and other AI accelerators. You'll also leverage various tools for simulation and performance modeling to optimize hardware configurations according to high-performance machine learning requirements.

Yes, OpenAI embraces a hybrid work model for the HW/SW Co-Design Engineer role, which includes three days in the office each week. This flexible approach allows you to collaborate with your colleagues in person while also enjoying remote work options, providing a balanced environment that fosters productivity and innovation.

To prepare for an interview for the HW/SW Co-Design Engineer position at OpenAI, focus on brushing up on your knowledge of hardware/software co-design principles, GPU and AI accelerator designs, as well as your coding skills in C/C++ and Python. Be ready to discuss specific projects where you've optimized hardware for machine learning applications and demonstrate how you collaborated with teams to align software needs with hardware capabilities.

When answering this question, focus on specific projects where you've had to balance both hardware and software requirements. Highlight your problem-solving skills in optimizing performance and programmability. Providing quantitative results, such as performance improvements, will make your response more impactful.

Discuss specific obstacles you encountered during hardware optimization, such as memory limits or performance bottlenecks. Detail the strategies you employed to overcome these challenges, aiming for a clear representation of your technical expertise and adaptability throughout the problem-solving process.

Emphasize your communication skills and demonstrate your understanding of machine learning needs. Talk about how you ensure alignment between hardware capabilities and the specific requirements from machine learning models, including regular meetings and iterative feedback loops.

Mention your hands-on experience with programming languages like CUDA, and provide examples of projects or tasks where you utilized these languages to manage GPU or AI accelerator functionalities. You should also address how your coding skills contributed to overall machine learning performance.

Describe your time management strategies, such as using project management tools or prioritizing tasks based on deadlines and project significance. Be sure to illustrate your ability to adapt and respond to shifting priorities while maintaining a focus on delivering high-quality work.

Provide a specific example where your insights or technical expertise led to changes in a vendor's product development plans. Discuss how you gathered cross-functional input and worked collaboratively to drive decisions that ultimately aligned with your organization’s goals.

Explain low precision formats in the context of machine learning and their importance for optimizing performance. Give examples of how you have implemented low precision techniques successfully in previous projects to achieve better efficiency without compromising model accuracy.

Demonstrate your knowledge of performance simulation by discussing specific tools you have used. Talk about how these simulations informed decisions about hardware configurations and optimizations, and share any significant outcomes you achieved from your simulations.

Emphasize your openness to feedback and your strategies for incorporating diverse perspectives into your work. Provide an example of a time when you received constructive criticism, detailing how you used it to make improvements or enhance collaboration.

Express your passion for the field by mentioning specific advancements that you find inspiring. Relate these advancements back to your work, discussing how they may impact future hardware development and machine learning capabilities, thus demonstrating your forward-thinking approach.