Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities.
As a Machine Learning Engineer on the AI Models team at Tenstorrent, you’ll bring up and optimize cutting edge AI models to run on our custom AI devices. You’ll experiment, optimize, and push boundaries while solving real world problems. If you love the craft of ML and want to work on models that are used in real world applications, you’ll feel right at home.
This role is hybrid, based out of Warsaw or Gdansk, Poland. We also consider remote candidates on a case-by-case status.
We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting.
- How to get real ML models to fly on a custom AI accelerator.
- Ways to optimize ML model performance, from application to silicon level.
- What it takes to go from research paper to production ready ML deployment.
- How to work alongside compiler, kernel, and hardware teams to drive new features, performance optimizations, and fixes.
- Confident with Python programming and hands-on experience with PyTorch for developing deep learning models.
- Driven by curiosity and a desire to experiment, always seeking to understand how complex systems work and how to make them better.
- Possess a deep understanding of ML model architectures, enabling optimization of both individual components and their interactions.
- Easy to work with and excited to collaborate across software and hardware teams.
- Experience bringing up state-of-the-art ML models on new hardware platforms.
- Ability to analyze performance bottlenecks, fine-tune architectures, and improve model accuracy and robustness.
- Familiarity with model optimization techniques (e.g., quantization, sparsity, attention) and key hardware features like matrix engines, caches, and memory hierarchies.
- A strong grasp of current ML research and the ability to translate insights into practical improvements.
