Optimize Deep Learning and Software Performance
Machine Learning Algorithm/SW Optimization Engineer
* Key Responsibilities:
* Understand and optimize the performance of deep learning libraries through in-depth knowledge of AI frameworks, algorithms, models, and related hardware.
* Set up AI workloads, analyze, and develop optimization techniques for existing and future compute architectures in collaboration with stakeholders.
* Explore new algorithms and optimizations for custom architectures to enable SW-HW co-design.
* Stay current with the latest advancements in deep learning literature to implement state-of-the-art algorithms.
* Communicate progress within research projects to stakeholders and adapt as needed.
* Participate in the EuroHPC DARE project to advance Europe's independence in high-performance computing and AI.
What We Offer:
* A leading research center in nanotechnology offers a competitive salary with fringe benefits, recognizing your valuable contributions.
* An inclusive, multicultural, and innovative environment with opportunities for personal and professional growth through imec.academy.
* The chance to join a team of experts in machine learning and software optimization.
* Collaborative working relationships with stakeholders to achieve common goals.
Requirements:
* PhD in Computer Science, Engineering, Mathematics, or a related field.
* Proficient in parallel programming, CUDA, and Python.
* Experience with Deep Learning Frameworks (PyTorch, TensorFlow, Jax).
* Experience with distributed training frameworks (Ray, Dask, PyTorch Lightning).
* Knowledge of optimization techniques like quantization, pruning, and distributed training.
* Experience in software performance analysis and hardware optimization.
* Experience with AI compute architecture optimization is a plus.
* Strong communication skills and a team-oriented mindset.
* Fluent in English, both spoken and written.