Founding Engineer (Machine Learning)

San Francisco, CA /
Engineering /
Full-time (Remote)
Streaming technologies are changing the data landscape and every application that produces and consumes data. Yet, most machine learning models, whose performance is tightly coupled with data quality and data freshness, are still in the batch paradigm.

Claypot unifies streaming and batch systems to make it easier and cheaper for companies to do online prediction, continuous evaluation, and continual learning. Our solution can be especially helpful for problems in fast changing environments such as recommender systems, e-commerce, fintech, and logistics.

Claypot AI was founded by Zhenzhong Xu and Chip Huyen. We're well-funded and working with cool companies!

For more discussion on the problem we're tackling, see Machine learning is going real-time and The Four Innovation Phases of Netflix’s Trillions Scale Real-time Data Infrastructure.

We're looking for great machine learning engineers and data scientists to be the foundation of our engineering team. We hire remotely anywhere in the world, as long as you can be available 8.30am - 12pm (PT) for synchronous communication. We plan to bring everyone together a few times a year to hang out and eat tasty food when it's safe to travel 🛫

What you'll do:

    • Design and develop an ML platform that you yourself would want to use to deploy ML models.
    • Evaluate ML tasks, models, metrics, techniques, etc. that we should support.
    • Incorporate ML and data science best practices into our platform.
    • Lead our open-source strategy.
    • [Optional] Engage with customers to understand their pain points and turn these insights into actionable items.
    • Lay the foundation for and grow a great engineering team.

We're building a platform to help data science teams deploy, manage, evaluate, and update ML models in real-time. We're looking for machine learning engineers who care about:

    • Deploying ML for real-world applications.
    • Improving the developer experience for data scientists / ML engineers.
    • Ensuring model quality in production via offline/online model evaluation.
    • Product mindset. What you build is customer-facing, so empathy with customers' pain points will be very helpful!
    • Strong communication skills. Because our team consists of engineers from multiple areas of expertise, we need everyone to communicate with each other, not shy away from writing design docs, identifying blockers, and requesting help and resources.

What makes Claypot AI special?

    • A culture of transparency, collaboration, and ownership
    • A very high bar for engineering craftsmanship
    • Expertise in both distributed systems and machine learning
    • A strong community
    • An opportunity to win over a large, growing, yet untapped market for fast ML delivery

What will you get?

    • Competitive compensation package
    • Flexible remote-first culture with options for in-person collaboration
    • Learn how to build a startup from the ground up
    • Public speaking opportunities
    • An environment for you to grow into the career you want

You'll stand out if you:

    • Have developed and deployed ML models in production.
    • Have worked with MLOps tooling such as MLflow, SageMaker. We might ask you about your favorite ML tool, and why.
    • Are familiar with concepts such as model store, feature store, monitoring & observability, data distribution shifts, continual learning, etc.
    • Are familiar with various ways to deploy ML models (e.g. online/batch prediction).
    • Are ready to take ownership and iterate quickly.
    • Are motivated by learning and growth
The job descriptions below are to give a sense of the challenges we're working on. As the company grows, you can define the role that you want with us. We believe in creating an environment for people to grow into their full potential and create the most impact for the team, not squeezing people to fit into job descriptions.

If you're interested in joining us but don't find a job description that fits you, reach out still!