Machine Learning Engineer, AI Models
Software Engineering, Data Science
Boston, MA, USA
Posted on Thursday, September 7, 2023
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 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
- 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.
- 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++ is a plus 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.
If you're passionate about AI, excited to tackle novel challenges, and motivated by the prospect of shaping the future through innovation, we encourage you to apply. Join us in our journey to redefine what's possible with AI models and make a lasting impact on the world of technology.
Boston, Austin, Santa Clara, Toronto, UK
Tenstorrent offers a highly competitive compensation package and benefits, and we are an equal opportunity employer.
Due to U.S. Export Control laws and regulations, Tenstorrent is required to ensure compliance with licensing regulations when transferring technology to nationals of certain countries that have been sanctioned by the U.S. government.
As this position will have direct and/or indirect access to information, systems, or technologies that are subject to U.S. Export Control laws and regulations, please note that citizenship/permanent residency information and/or documentation will be required and considered as Tenstorrent moves through the employment process.