ABOUT COMMUNITY ENERGY LABS

Community Energy Labs (CEL) is a female-founded and led energy technology company, ️ with a mission to enable affordable decarbonization of community buildings by 2030. We enable buildings to use energy when clean sources of power are available and to use less energy overall by making smart decisions about when and how energy is used. Our goal is to advance the affordability of clean building control for low-income and underserved building owners using AI. We primarily work with communities and schools, whose buildings make up nearly 30% of the commercial floor space in the US!

Our software combines cutting-edge control algorithms that use machine learning and thermal energy models along with traditional control techniques to make it happen. We combine that with an intuitive user interface that doesn’t require a Ph.D. in mechanical engineering to make saving money and the planet easy for everyone - even cash-strapped schools with overworked building operators.

CEL's core AI-powered clean building control platform concept was a regional winner of CleanTech Open's 2020 international accelerator, an overall winner in the 2020 Madrona Venture Labs Go Vertical challenge, a 2021 impel+ building innovator, EPRI Incubate Energy 2021 cohort member and 2021 US Department of Energy SBIR awardee. Follow CEL at Twitter and on LinkedIn.

ABOUT THE ROLE

The Full Stack Engineering role contributes to development of Community Energy Lab’s product stack by producing the smart controls needed to help our customers reach their goals, whether that’s saving money or saving the planet.

This role will be responsible for understanding the data that needs to be collected; building and containerizing the recommended control algorithms to be used, and measuring the quality of the solution. This role will also help to determine the ingress of data and the egress of control directives from these machine-learning-based algorithms and other design activities that help to define how the software solution as a whole is architected to produce the best results from our control algorithms.

Although we list specific technologies here and there, we’re most interested in hiring smart people who like solving problems, experimenting with different technologies and approaches to a problem, and then converging on a solution and moving forward. The ideal candidate will be comfortable building prototypes that help us learn what our Minimum Lovable Product is and the feasibility of various solutions before the final architecture comes into play. Apply here.

This is an opportunity to join and help scale an early-stage startup in the clean energy industry.

RESPONSIBILITIES

• Reviewing relevant literature regarding data collection, building modeling, and optimization techniques.
• Determining the right building attributes and equipment data to collect at the frequency needed to achieve results.
• Developing APIs and data access to our various components such as our graph database, time series data, and devices in the field via AWS IoT,
• Collecting and constructing data inputs from: building attributes; streams like dynamic pricing and weather; and equipment and sensor data.
• Using publish/subscribe mechanisms to deliver data to and from our machine-learning based control algorithms to other areas of the system.
• Packaging different types of optimization algorithms, from Model Predictive Control (MPC) to Reinforcement Learning (RL) to hybrid solutions from prototyped solutions.
• Help to design M&V techniques using various statistical methods to help understand the effectiveness of the solution as well as identify areas of improvement.
• Building tests to maintain a high-level of quality in the system.
• Integrating with databases of all types: relational, GraphDB, and TimeSeries.

REQUIREMENTS

• Bachelor's degree in computer science, mathematics, or related
• 3+ years of experience working with Python.

DevOps Experience:

• Containerizing solutions for easy deployment.
• Working in Linux-based operating systems without a windowing system.
• Deploying solutions in our AWS-based cloud environment using EC2, Lambda,, S3, Cloudwatch, and others as needed.
• Familiarity with distributed systems architecture: datasets, parallel data processing, and distributed training.
• Understanding of storage, network, and compute resources and their speed and cost tradeoffs.
• Commitment to stage-gate testing from data validation to model performance and code integration.
• Value and stay on top of security, privacy best practices

YES, THAT MEANS YOU!

We are intentionally a diverse group of people, and we're eager to keep growing that diversity. If our mission and this job speak to you and you have the interest and ability to work smart, learn, and grow with us then we want you to apply for this job. Even if this is not the exact right opportunity for you, we want to know about you and keep you in mind for future posts.

Community Energy Lab is an Equal Opportunity Employer. All applications will receive consideration for employment without regard to legally protected characteristics.