<p>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 maximizes 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 are contracted to 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.</p> <p>As a Sr. Algorithms Engineer, you will be responsible for steering the evolution of Autobidder's bidding and automation algorithms. This includes rapid iterations when entering new markets and devising sophisticated algorithmic approaches to optimize revenues and increase automation in advanced markets. You will develop deep expertise in electricity markets and leverage your technical skills to craft algorithms that help Autobidder deliver best-in-class performance. You will be intimately familiar with the performance and operational nuances of assets operated by Autobidder and will serve as the feedback loop between operational learnings and algorithmic advancements to ensure our algorithms deliver real-world value. You will own production systems and be responsible for their performance, reliability, and availability. Your work will help proliferate battery storage and renewable projects around the globe.</p> <ul> <li>Algorithm Development:Design, implement, and maintain production code for sophisticated bidding, optimization, simulation, and forecasting algorithms </li><li>Research and Innovation:Prototype, benchmark, deploy and monitor advanced algorithmic features that account for uncertainties in prices and clearing outcomes, optimally allocate quantities to maximize risk adjusted revenues, reason about interactions with strategic competitors, account for influence of quantity on clearing prices, etc. for large fleets of utility-scale storage assets and VPPs </li><li>Domain Expertise:Become an expert inelectricity markets and grid,and the various facets of operating in them </li><li>Technical Leadership:Guide algorithmic decisions to balance performance and complexity and make thoughtful design and infrastructure choices that facilitate a positive developer experience in the long run </li><li>Tooling and Simulation:Develop tooling and simulation systems to monitor and track field performance of assets. Define metrics to quantify, track, and improve specific areas of performance, and drive algorithm changes to enhance asset performance under management </li><li>Cross-Functional Collaboration:Work with ML engineers, traders, market analysts, and software engineers to ensure algorithms drive end-to-end value </li><li>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 </li><li>Experience in building real-world products and solutions using numerical optimization technology (LP, MILP, nonlinear optimization, etc.) andsolving real-world optimization problems using solvers such as Gurobi, XPRESS, GLPK, CPLEX, etc. </li><li>Expertise with relevant Python libraries such as cvxpy, pyomo, pandas, numpy, sklearn, streamlit, and more </li><li>Demonstrated experience in developing and maintaining production software </li><li>Self-motivation, enthusiasm for learning and collaboration, and a passion for working in the clean energy space </li><li>Strong preference for domain expertise in forecasting, analysis, or trading in electricity markets (e.g., ISOs like ERCOT, CAISO, PJM, AEMO, UK National Grid) </li><li>Prefer experience with working on cloud hosted systems and related tooling like compute services (EC2, GCP Compute Engine), container orchestration (Kubernetes, Docker), etc. </li><li>Preferacademic trainingin numerical optimization, operationsresearch, stochastic control, optimal control, computational finance, andrelatedmathematical fields </li><li>Prefer prior experience in researching, developing, and deploying new algorithmic strategies to solve novel optimization problems, eg. decision-making under uncertainty, scenario optimization, MDPs, financial risk modeling, complementarity problems, etc. </li><li>Prefer familiarity with a range of machine learning and statistical algorithms, including gradient-boosted decision trees, the ARIMA family, transformers, recurrent networks, etc. </li></ul>