ML Frameworks Software Development Engineer - vLLM (amd)

Job Description

WHAT YOU DO AT AMD CHANGES EVERYTHING

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. 

AMD together we advance_

THE ROLE: 

We are looking for an AI Software development engineer who is passionate about building and optimizing deep learning applications and machine learning frameworks.
Be a part of an AMD development team and the open-source community to design, develop, test and deploy improvements to make us the best platform for generative artificial intelligence and deep neural networks. Incredibly talented industry specialists.

THE PERSON: 

The ideal candidate has strong technical and analytical skills in high performance C++. Able to communicate effectively and work optimally with different teams across AMD. Experience in tensor libraries or machine learning execution engines is a plus.  

KEY RESPONSIBILITIES: 

• Optimize key deep learning models on AMD GPUs

• JOB IS FROM: italents.netVIEWDevelop and finetune inference and serving engines for large language models
• Collaborate with AMD’s architecture specialists to improve future products
• Collaborate with operator libraries team to analyze and optimize training and inference for deep learning
• Work in a distributed computing setting to optimize for both scale-up (multi-accelerator) and scale-out (multi-node) systems
• Optimize the entire deep learning pipeline including graph compiler integration
• Participate in new ASIC and hardware bring ups
• Apply your knowledge of software engineering best practices

PREFERRED EXPERIENCE: 

• Ability to work independently, define project goals and scope, and lead your own development effort
• Deep Learning experience or knowledge - Natural Language Processing, Vision, Audio, Recommendation systems
• Programming in High-Performance Domain-Specific Languages for GPU or ML ASIC Computing (HIP, CUDA, OpenCL, Triton, KNYFE, Mojo)
• Using debuggers (LLDB, GDB) and profilers on heterogeneous hardware
• PyTorch or TensorFlow
• Familiarity with ML Graph Compilers (Glow, FX, XLA) is a plus
• Knowledge of the lower levels of typical ML workflows from MLIR to LLVM all the way down to assembly language(s) is a plus

ACADEMIC CREDENTIALS: 

• Master’s or PhD in Computer Science, Computer Engineering or related fields

#LI-JG1

At AMD, your base pay is one part of your total rewards package.  Your base pay will depend on where your skills, qualifications, experience, and location fit into the hiring range for the position. You may be eligible for incentives based upon your role such as either an annual bonus or sales incentive. Many AMD employees have the opportunity to own shares of AMD stock, as well as a discount when purchasing AMD stock if voluntarily participating in AMD’s Employee Stock Purchase Plan. You’ll also be eligible for competitive benefits described in more detail here.

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.