Engineer, ML Models
About The Position
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 Toronto, ON; Austin, TX; Santa Clara, CA, with the opportunity to be remote on a candidate by candidate basis. 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.
Requirements
- 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, with the ability to optimize both individual components and overall model performance.
- Easy to work with and excited to collaborate across software and hardware teams.
- Hands-on experience bringing up state-of-the-art ML models on new hardware platforms.
- Strong debugging instincts to investigate performance issues, tune architectures, and boost model accuracy and robustness.
- Working knowledge of model optimization techniques—like quantization, flash attention, kernel fusing—as well as hardware features such as matrix engines and memory hierarchies.
- A curiosity-driven mindset that stays current with ML research and brings practical insights to real-world engineering challenges.
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
No Education Listed
