About Keeling Labs

Who We Are: We’re an applied ML research company squarely focused on solving climate change.

• We recently closed a $1.5M seed round

• We’re backed by Y Combinator (W23)

• Our founder previously started the Battery Data Science Team at Rivian

What We’re Building: We’re optimizing how large batteries in the grid buy and sell energy so that the grid can run on more renewables. To do this, we’re developing self-optimizing, self-adapting systems using reinforcement learning.

• You can learn more about how our code directly impacts the grid in our blog post here

Why It Matters: As the grid increasingly shifts to renewable energy from wind and solar, more and more large battery systems are needed to store that energy for times when there’s huge demand for energy but not enough supply. As a society, we have all the physical battery technology needed to do this, but the software technology that figures out when and how to optimally use the battery is still in its infancy.

There’s going to be a 15x increase in these large batteries by 2030, and they’re currently growing at 50% YoY. As the grid is fundamentally altered by the addition of these batteries, we need to ensure that we’re utilizing all of them to their full potential.

About the Role

We're hiring for a skilled, tech-forward ML engineer to design and enhance our core training, experimentation, and deployment infrastructure. The ideal candidate will have built this from scratch in a previous role for an applied ML application solving an important problem.

What You’ll Be Responsible For:

• Design, build, and enhance core training infrastructure for our RL agents

• Closely collaborate with research scientists working on new agent designs

• Optimize large-scale training for cost, speed, and scale

• Develop CI/CD pipelines and ETL pipelines for a variety of applications

Requirements:

• 2+ years of industry experience building ML training infrastructure from scratch

• Experience architecting and implementing CI/CD pipelines

• Collaborative, results-oriented mindset, ability to work in a fast-paced environment, strong communication skills

• Frameworks: AWS (required), Kubernetes (required), Airflow (required), Docker (required), Spark (ideal), Pandas (required)

• Languages: Python (required), C/C++ (ideal)

• Willing and able to work in-person in Los Angeles