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Semiconductor, Hardware, Technology

Director Software Engineering - AI/ML Compilers

California, United StatesOnsiteFull TimeDirector$232,000–$348,000 /yrPosted 2 months agoVisa sponsorship available

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Role summary

AMD is seeking a Director of Machine Learning to lead their AI/ML Model Compiler and Applications team. This role focuses on distributed training of large models on NPUs, aiming to improve training efficiency and innovate in generative AI. The ideal candidate will have experience delivering ML compiler solutions, understanding NPU hardware, defining scalable algorithms, and leading a team. Responsibilities include research, design, and implementation of novel methods for efficient Generative AI, developing ML compilers for low power inference, and optimizing models across AMD platforms. Collaboration with software and hardware teams, and engagement with open-source communities are key. A PhD or Master's degree in a related field is required.

WHAT YOU DO AT AMD CHANGES EVERYTHING
At AMD, our mission is to build great products that accelerate next-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover the real differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond.
Together, we advance your career.
The Role
We are looking for a Director, Machine Learning to join our AI/ML Model Compiler and Applications team. If you are excited by the challenge of distributed training of large models on Neural Processing Unit (NPUs), and if you are passionate about improving training efficiency while innovating and generating new ideas, then this role is for you. You will be part of a world class team focused on addressing the challenge of training generative AI.
The Person
The ideal candidate should have experience with delivering ML compiler solutions, understanding of NPU hardware architecture, defining scalable algorithms that enables efficient problem mapping, interfacing with customers to understand the requirements and driving a team to develop solutions.
Key Responsibilities

  • Lead a growing team focused on research, design, and implement novel methods for efficient Generative AI.
  • Experience in developing ML compilers for low power inference architecture
  • Experience in all 3 stages of ML compiler- front end, middle end and backend
  • Experience in defining optimized tiling algorithms, fusion algorithms at different memory hierarchy
  • Influence the direction of AMD AI platform.
  • Collaborate across teams with various groups and stakeholders.
  • Propose and apply innovative model quantization, sparsity, and acceleration algorithms on various AMD platform, e.g., GPU/CPU/AIE.
  • Collaborate with software and hardware team to E2E co-optimize model performance.
  • Work with open-source framework and community (e.g., Pytorch, Huggingface) to integrate AMD optimized models and libraries.

Preferred Experience

  • Experience with ML frameworks such as Onnx, PyTorch, or TensorFlow.
  • Experience with distributed training and distributed training frameworks, such as DeepSpeed.
  • Experience with LLMs or computer vision, especially large models, is a plus.
  • Excellent C++ and Python programming skills, including debugging, profiling, and performance analysis.
  • Experience with ML pipelines.
  • Strong communication and problem-solving skills.
  • Strong leadership and management skills.

Academic Credentials

  • A PhD or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.

*Benefits offered are described:*
AMD benefits at a glance.
*AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.*
*AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.*
*This posting is for an existing vacancy.*

Sample AMD interview questions

  • 1

    Develop a service for managing distributed locking.

    system designmedium
  • 2

    Merge a New Interval Merge a new interval into a list of non-overlapping intervals. Input: intervals = [[1,2],[3,5],[6,7],[8,10],[12,16]], newInterval = [4,8] Output: [[1,2],[3,10],[12,16]] Explanation: The new interval overlaps with [3,5], [6,7], and [8,10], merging them all into the unified block [3,10].

    codingmedium
  • 3

    Aggressive Cows Maximize the minimum distance between aggressive cows in stalls. Input: stalls = [0,4,3,7,10,9], cows = 3 Output: 4 Explanation: Placing the cows at positions 0, 4, and 10 yields a maximum possible minimum distance of 4 between any two cows.

    codingmedium
  • 4

    Longest Consecutive Sequence Determine the length of the longest consecutive elements sequence. Input: nums = [0,3,7,2,5,8,4,6,0,1] Output: 9 Explanation: The longest consecutive sequence is 0 through 8 (length 9), utilizing a hash set to check connectivity in linear time.

    codingmedium
  • 5

    Reverse Nodes in k-Group Reverse nodes in k-group in a linked list. Input: head = [1,2,3,4,5], k = 3 Output: [3,2,1,4,5] Explanation: The first 3 elements are reversed, while the remaining 2 are left untouched since they don't form a complete group.

    codingmedium

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