The mission of the Autobidder team is to accelerate the world's transition to sustainable energy by maximizing the value of storage and renewable assets. We achieve this by building state-of-the-art software products for monetizing front-of-the-meter and behind-the-meter energy storage systems. Our flagship product, Autobidder, is an end-to-end automation suite for wholesale electricity market participation of grid-connected batteries and renewable resources that maximize revenues by optimally bidding in all available revenue streams in these markets. We are a multidisciplinary algorithmic trading team with expertise in machine learning, numerical optimization, software engineering, distributed systems, electricity markets, and trading. We have a proven track record of operating storage assets and delivering high revenues in both utility-scale and Virtual Power Plant (VPP) settings. Our products currently manage over 7GWh of energy storage worldwide and have returned over $420 million in trading profits, and we're slated for rapid growth on the horizon.

• Lead the design, development and evolution of our internal forecasting platform
• Conduct creative research to identify new machine learning approaches that improve metrics and incorporate these into our platform
• Identify and integrate new data sources to enhance model performance
• Design scalable and reliable data pipelines to productionize and monitor both new and existing models
• Become an expert in electricity price formation and market dynamics
• Deliver various types of electricity market-related forecasts including energy and ancillary service prices, load, regulation throughput, and reserve deployments for use in downstream algorithms
• Mentor and develop a growing team of exceptional machine learning engineers into one of the leading electricity market forecasting teams
• Collaborate with optimization engineers, traders, market analysts, and software engineers to ensure forecasts drive end-to-end value

• Proficiency in Python with at least 6 years of experience in software development, familiarity with software development practices, writing production-quality code, and agile development
• Experience with a variety of forecasting algorithms and approaches, including statistical, regression, and deep learning algorithms
• Experience with cloud-hosted systems and related tooling, including computing services AWS EC2, Google Compute Engine, container orchestration Kubernetes, Docker, and database and data warehouse platforms Amazon RDS, Google BigQuery
• Expertise with relevant Python libraries such as pandas, numpy, xgboost, lightgbm, pytorch, sklearn, plotly, seaborn, and streamlit
• Demonstrated experience in developing and maintaining production ML platforms
• Intrinsic motivation and passion for learning, collaboration, and working in the clean energy space
• Degree in Mathematics, Machine Learning, Statistics, or equivalent experience
• Domain expertise in forecasting, analysis, or trading in electricity markets ERCOT, CAISO, PJM, AEMO, and UK National Grid
• Experience in shipping production models in time series forecasting or reinforcement learning
• Familiarity with forecasting libraries such as Nixtla, Pytorch-Forecasting or Darts