Jobs MTS AI/ML Runtime Software Systems Engineer
MTS AI/ML Runtime Software Systems Engineer
We care deeply about transforming lives with AMD technology to enrich our industry, our communities, and the world. Our mission is to build great products that accelerate next-generation computing experiences – the building blocks for the data center, artificial intelligence, PCs, gaming and embedded. Underpinning our mission is the AMD culture. We push the limits of innovation to solve the world’s most important challenges. We strive for execution excellence while being direct, humble, collaborative, and inclusive of diverse perspectives.
THE ROLE:
We are looking for a dynamic, energetic Lead / Senior Runtime Systems Design Engineer to join our growing team in AI group. As a part of this role, you will be responsible for designing, developing, and optimizing the runtime stack components on AMD’s XDNA Neural Processing Units that power cutting edge generative AI models like Stable diffusion, SDXL-Turbo, Llama2, etc. Your work will directly impact the efficiency, scalability, and reliability of our ML applications. If you thrive in a fast-paced environment and love working on cutting edge machine learning inference applications, this role is for you.
KEY RESPONSIBILITIES:
- Design and implement C++ runtime wrappers, APIs, and frameworks for ML model execution.
- Collaborate with kernel developers to integrate ML operators seamlessly into high level ML frameworks like ONNX.
- Model Loading and Inference:
- Interface with ONNX / Pytorch runtime engines to deploy the model on CPUs.
- Develop efficient model loading mechanisms to minimize startup latency.
- Implement high-performance inference engines for Client GenAI workloads such as Llama2-7B, Stable diffusion, SDXL-Turbo etc.
- Resource Management:
- Manage CPU, and memory resources effectively during model execution.
- Handle resource allocation for ML deployments across different tenants.
- Scalability and Optimization:
- Architect optimized CPU alternative implementation for ML operators that are not supported on NPUs.
- Monitoring and Diagnostics:
- Build tools to track resource utilization, bottlenecks, and anomalies.
- Enable detailed profiling and debugging tools for analyzing ML workload latency.
- Collaboration and Documentation:
- Work closely with ML researchers, software engineers, and Architecture teams to understand the performance requirements.
- Document design specs, APIs, and follow coding guidelines like creating PRs, and doing diligent code reviews.
QUALIFICATIONS:
- Strong programming skills in C++, Python.
- Experience with ML frameworks (e.g., TensorFlow, PyTorch) is required.
- Experience with ML models such as CNN, LSTM, LLMs, Diffusion is a must.
- Experience with ONNX, Pytorch runtime integration is a bonus.
- Familiarity with containerization (Docker, Anaconda, etc) is good to have.
- Excellent problem-solving abilities and a passion for performance optimization.
ACADEMIC CREDENTIALS:
- Bachelor’s, Master’s, or PhD degree in Computer Science, Electrical Engineering, or related fields.