Machine Learning Research Engineer (LLMs & AI Systems) at Tenstorrent

Tenstorrent is building next-generation AI systems that push the boundaries of model training, inference, and large-scale distributed compute. The ML Models team sits at the intersection of cutting-edge AI research and high-performance hardware, bringing state-of-the-art machine learning models to life on Tenstorrent’s custom AI accelerators. From training large language models to optimizing inference performance at scale, this team works across the full stack to turn breakthrough research into production-ready AI systems. If you are passionate about advancing the frontier of AI research, inference and training optimizations, this is an opportunity to shape how future AI models are developed and deployed.

This role is hybrid, based out of Toronto, ON and Boston, MA.

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.

Who You Are

• Strong Python and PyTorch experience developing and training deep learning models.
• Deep understanding of ML architectures, LLM training, and inference optimization.
• Hands-on experience training large-scale machine learning models.
• 4+ years of industry and/or academic experience in ML research and LLM development.
• PhD, published research, or experience with speculative decoding is highly valued.

What We Need

• Lead research and development efforts focused on LLM training and inference optimization.
• Train, evaluate, and optimize state-of-the-art AI models on Tenstorrent hardware.
• Improve performance through techniques such as speculative decoding, quantization, kernel fusion, flash attention, and distributed training.
• Investigate system bottlenecks and collaborate cross-functionally to drive performance improvements.
• Translate cutting-edge ML research into scalable, production-ready solutions.

What You Will Learn

• How to optimize AI models on custom AI accelerators from application to silicon.
• How large-scale ML systems are deployed, tuned, and scaled in production.
• How hardware, compiler, kernel, and ML teams collaborate to maximize performance.
• The challenges and tradeoffs of scaling modern AI workloads across custom hardware.

Compensation for all engineers at Tenstorrent ranges from $100k - $500k including base and variable compensation targ