As a Machine Learning Engineer focused on AI models, you will play a pivotal role in the development and deployment of advanced neural networks and machine learning models. Your expertise in Python and PyTorch will be critical in designing, training, and fine-tuning complex models that will be applied to solve real-world challenges. The ideal candidate will not only possess strong technical skills but also exhibit a deep curiosity, investigative mindset, and a passion for staying up-to-date with the latest advancements in the AI landscape.

This role is hybrid, based out of Toronto, ON; Boston, MA; Santa Clara, CA; or Austin, TX.

Responsibilities:

• Collaborate with cross-functional teams to understand project requirements and translate them into effective AI solutions.
• Design, implement, and iterate on neural network architectures using PyTorch to achieve optimal performance on diverse tasks.
• Train, validate, and fine-tune machine learning models using relevant datasets to ensure high accuracy and robustness.
• Investigate and troubleshoot model performance issues, iterating on models and techniques to continuously improve results.
• Understand how models map onto the Tenstorrent devices through compilation steps and kernels, investigate any gaps in functionality and performance, propose innovative solutions to mitigate any issues.
• Stay current with the latest research and developments in the field of machine learning and AI, applying relevant advancements to our projects.
• Contribute to the design and implementation of scalable and efficient AI pipelines.
• Work closely with engineers to integrate AI models into production systems.

Experience & Qualifications:

• Bachelor's or higher degree in Computer Science, Engineering, or related field (or equivalent practical experience).
• Strong proficiency in Python programming and hands-on experience with PyTorch for developing and training deep learning models.
• Demonstrated experience in designing, training, and deploying neural networks for various applications.
• Solid understanding of machine learning fundamentals, including supervised and unsupervised learning techniques.
• Excellent problem-solving skills and the ability to approach challenges with creativity and a strong analytical mindset.
• Familiarity with debugging techniques and a knack for identifying issues and finding effective solutions.
• Experience with C++ and kernel programming, knowledge in GPU/AI-accelerator architectures are pluses but not mandatory.
• A self-motivated individual with a strong desire to learn, explore, and excel in a fast-paced, innovative environment.
• Strong communication skills and the ability to work collaboratively in a team setting.