Principal Product Manager, ML

About The Role

In this role, you will be responsible for productizing the most critical ML use cases for our company.  

You will work closely with Product leadership and our ML Research and Applied ML teams to identify the most promising areas within the industry and research community for us to go after, balancing business value for our customers and ML thought leadership for Cerebras.  

You will translate abstract neural network requirements into concrete deliverables for the Engineering team and work with cross-functional partners to establish roadmaps, process, success criteria, and feedback loops to continuously improve our products.  

This role combines both the highly technical with the highly strategic. Successful candidates will have deep understanding of machine learning and deep learning concepts, a familiarity with common modern models (in particular in the LLM space), and the ability to understand the mathematical foundations behind them. Ideal candidates can go beyond model understanding to see the connections and commonalities across different types of neural networks in different application domains. They will also be close followers of the recent developments in deep learning and have a point of view on which types of models may be widely used within the next 1, 3, 5 years.  

At Cerebras, we're proud to be among the few companies globally capable of training massive LLMs with over 100 billion parameters. We're active contributors to the open-source community, with millions of downloads of our models on Hugging Face. Our customers include national labs, global corporations across multiple industries, and top-tier healthcare systems. This month, we announced a multi-year, multi-million-dollar partnership with Mayo Clinic, underscoring our commitment to transforming AI applications across various fields. We are already booking hundreds of millions of dollars in revenue each year, with a strong growth trajectory.  

As the Cerebras ML PM, you will be in the pilot’s seat, driving the transformational role of AI in multiple different industries and getting to work with some of the largest and most interesting datasets in the world alongside a world-class ML research and engineering team.  

Responsibilities

• Gain a deep understanding of deep learning use cases across different industries and organization types, integrating market analysis, research, and user research studies.   
• Develop, define, maintain, and own the product roadmap for the neural network architectures and machine learning methods supported by the Cerebras platform. 
• Work directly with current and prospective end users to define and deeply understand market requirements for AI models used in industry and research. 
• Work directly with engineering to define software requirements, priorities, and staging for ML network support, specifying associated features, and bringing features from concept to launch. 
• Define success metrics and testing criteria for successful enablement of these applications, including taking neural network architectures from their research paper descriptions, breaking them down into their detailed components, and articulating the accuracy and performance expectations of the ML community. 
• Work as a partner to Marketing, Product Marketing, and Sales by supporting feature documentation and defining ML user need, competitive landscape, our product value proposition, and user story.
• Work across Product, Engineering, and business leadership to help define our product go-to-market approach to maximize value to users and expand our user community over time.
• Communicate roadmaps, priorities, experiments, and decisions clearly across a wide spectrum of audiences from internal customers to executives.

Skills And Qualifications

• Bachelor’s or Master’s degree in computer science, electrical engineering, physics, mathematics, a related scientific/engineering discipline, or equivalent practical experience.
• 3-10+ years product management experience, working directly with engineering teams, end users (enterprise data scientists/ML researchers), and senior product/business leaders.
• Strong fundamentals in machine learning/deep learning concepts, modern models, and the mathematical foundations behind them; understanding of how to apply deep learning models to relevant real-world applications and use cases.
• Experience working with a data science/ML stack, including TensorFlow and PyTorch.
• An entrepreneurial sense of ownership of overall team and product success, and the ability to make things happen around you. A bias towards getting things done, owning the solution, and driving problems to resolution.
• Outstanding presentation skills with a strong command of verbal and written communication.

Preferred Skills And Qualifications

• Experience developing machine learning applications or building tools for machine learning application developers.
• Prior research publications in the machine learning/deep learning fields demonstrating deep understanding of the space.