Application Performance Engineer, Technical Lead / Principal

At d-Matrix, we are focused on unleashing the potential of generative AI to power the transformation of technology. We are at the forefront of software and hardware innovation, pushing the boundaries of what is possible. Our culture is one of respect and collaboration.

We value humility and believe in direct communication. Our team is inclusive, and our differing perspectives allow for better solutions. We are seeking individuals passionate about tackling challenges and are driven by execution.  Ready to come find your playground? Together, we can help shape the endless possibilities of AI. 

Location:

Hybrid, working onsite at our Santa Clara, CA headquarters 3 days per week.

Application Performance Engineer – Technical Lead/Principal

d-Matrix is seeking senior engineers who are obsessed with performance analysis and optimization to extract the best inference performance on our hardware. The ideal candidate will be comfortable working across all layers of the hardware and software stack, from the architecture to compilers, kernels and frameworks.  This is a deeply technical, cross-functional role where you will work with external developers, researchers and engineers using our products and collaborate closely with internal R&D and product teams.  

What You Will Do:

1. Provide expert guidance and support to customers with workload performance analysis, debugging, profiling, and implementing optimizations on d-Matrix hardware and software

2. Develop tools and technical collaterals (developer guides and other supporting materials) to simplify user experience with workload analysis and optimization

3. Profile and analyze existing and emerging workloads on simulators and state-of-the-art hardware. 

4. Benchmark performance for generative AI model inference on state-of-the-art hardware and software platforms

5. Profile and analyze workloads in potential new product areas to help guide roadmap decisions

What You Will Bring:

1. Engineering degree in Electrical Engineering, Computer Engineering, Computer Science, or related field, with 10+ years of relevant experience in AI/ML hardware, software and infrastructure.Strong background in deep learning and neural networks, in particular generative AI and Academic experience in computer architecture, hardware software co-design, performance modeling

2. Proven experience analyzing and tuning inference performance on GPUs.

3. Experience with processor and system-level performance modeling.

4. Experience with common deep learning software packages like PyTorch, vLLM, etc. as well as understanding of model compilation and execution stack.  

5. Experience with OpenAI Triton and/or CUDA.

6. Strong programming skills in C++, Python.  

7. Excellent communication and presentation skills

8. Experience in customer engineering and field support for enterprise-level AI and datacenter products, with a focus on AI/ML software and generative AI inference, preferred.

Equal Opportunity Employment Policy

d-Matrix is proud to be an equal opportunity workplace and affirmative action employer. We’re committed to fostering an inclusive environment where everyone feels welcomed and empowered to do their best work. We hire the best talent for our teams, regardless of race, religion, color, age, disability, sex, gender identity, sexual orientation, ancestry, genetic information, marital status, national origin, political affiliation, or veteran status. Our focus is on hiring teammates with humble expertise, kindness, dedication and a willingness to embrace challenges and learn together every day.

d-Matrix does not accept resumes or candidate submissions from external agencies. We appreciate the interest and effort of recruitment firms, but we kindly request that individual interested in opportunities with d-Matrix apply directly through our official channels. This approach allows us to streamline our hiring processes and maintain a consistent and fair evaluation of al applicants. Thank you for your understanding and cooperation.