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Sr. Machine Learning Engineer, Autobidder

2 months ago

Palo Alto, United States Tesla Full time
What to Expect

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 $330 million in trading profits, and we're slated for rapid growth on the horizon.

You will develop forecasting algorithms for Autobidder. You will research, prototype, evaluate and productionize new forecasts for electricity prices and other relevant market outcomes. You will ensure that forecasting improvements translate into trading revenue gains for our assets. Your work will be critical in maintaining best in best-in-class performance of Autobidder. You will own production systems and be responsible for their performance, reliability and availability. Your work will help proliferate the building of battery storage and renewable projects around the globe.

What You’ll Do
  • Design, develop, and revolutionize 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
What You’ll Bring
  • Proficiency in Python with at least 4 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