Machine Learning Innovation Architect

3 dni temu

Warszawa, Mazovia, Polska Advanced Micro Devices Pełny etat

As a Machine Learning Architect at AMD, you will play a crucial role in 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.

You will work with incredibly talented industry specialists and the very latest hardware and software technology to push the boundaries of what is possible in rendering.

Required Skills and Qualifications
  • Machine Learning Expertise: Expertise in Machine Learning, particularly focused on Model Creation and Model Architecture, including advanced techniques such as deep learning, reinforcement learning, and generative models.
  • Deep Learning Models: Expertise in either Diffusion or Generative Transformer based LLM models.
  • Programming Skills: Strong proficiency in Python programming for implementing machine learning algorithms, data preprocessing, and model evaluation.
  • Software Development Workflows: Comprehensive understanding of general software development workflows, including version control systems like Git, environment management tools like Docker, Conda, and continuous integration (CI) pipelines.
  • Communication Skills: Proficient in English, with excellent written and verbal communication skills for collaborating with team members and presenting findings or proposals.
  • Cross-Functional Collaboration: Collaborate with cross-functional teams including data scientists, engineers, and domain experts to understand requirements, develop prototypes, and deploy production-ready machine learning solutions.
  • Research and Development: Research and stay up-to-date with the latest advancements in machine learning algorithms, frameworks, and tools, incorporating best practices into model development and architecture design.
  • Model Optimization: Optimize machine learning models for deployment on various platforms including cloud infrastructure, edge devices, and embedded systems, balancing performance, resource constraints, and scalability requirements.
  • Experimentation and Evaluation: Conduct thorough experiments and evaluations to assess model performance, reliability, and robustness, employing techniques such as hyperparameter tuning, cross-validation, and A/B testing.
  • Documentation and Knowledge Sharing: Document code, methodologies, and findings comprehensively, ensuring reproducibility and knowledge sharing within the team and across the organization.
  • Mentorship and Team Growth: Mentor junior team members, providing guidance on machine learning concepts, programming techniques, and software development practices to foster skill development and team growth.

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