Sr. Algorithms Engineer, Grid Modeling, Autobidder

What to ExpectThe 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.As a Sr Algorithm Engineer (Grid Modeling), you/'ll play a pivotal part in developing advanced power flow models, including Security-Constrained Economic Dispatch (SCED) and Security-Constrained Unit Commitment (SCUC), tailored for energy trading applications. Your work will focus on generating alternative forward price views to serve as forecasts for bidding strategies. You/'ll collaborate with a dynamic team of Machine learning engineers, optimization engineers and market experts to build scalable, production-grade models that integrate fundamental power flow analysis with innovative data-driven approaches. This position offers the opportunity to drive real-world impact towards acceleration of sustainable energy. Expect a fast-paced environment where your expertise in power flow modeling and optimization will directly influence trading decisions.What You/'ll DoDesign and implement a Production Cost Model (PCM) with SCUC and SCED formulations to simulate electricity markets and generate forward price forecastsIdentify and leverage relevant data sources (e.g., grid topology, generation data, load forecasts, supply-side offer and demand-side bid curves, and market reports) while creatively simplifying or approximating unknown variables to ensure model robustness and accuracyDevelop robust ETL pipelines and curate comprehensive datasets to support the execution, validation, and performance evaluation of the PCM modelFormulate and solve complex optimization problems using mathematical programming techniques, ensuring models capture key constraints like transmission limits, ramp rates, and reserve requirementsIntegrate fundamental-based models with machine learning techniques to enhance predictive accuracy and adaptability in volatile energy marketsDevelop production-grade software in Python, own the grid modeling component end-to-end, including integration with data pipelines for real-time and batch processingCollaborate with cross-functional teams to validate models against historical data, perform sensitivity analyses, and iterate on formulations to improve trading outcomesContribute to the scalability and maintainability of Autobidder/'s algorithms, including debugging, testing, and deploying models in cloud-based environmentsWhat You/'ll BringProficiency in Python with at least 4 years of experience in software development, familiarity with software development practices and writing production-quality codeIndustry experience building power flow models, SCED, and SCUC for energy trading or related applications, with demonstrated expertise in ERCOT or similar wholesale marketsDeep understanding of the mathematical details of SCED, SCUC, and related optimization formulations like mixed-integer linear programming (MILP)Deep knowledge of energy market data sources, grid operations, and trading strategies, with the ability to simplify complex systems for practical implementationProficiency in Python for scientific computing and optimization, with hands-on experience using libraries like Pandas, NumPy, SciPy, and optimization solvers (e.g.Gurobi, XPRESS, GLPK, CPLEX, etc.)Excellent problem-solving skills, attention to detail, and the ability to work independently in a fast-paced, collaborative environmentPrefer experience combining fundamental power systems models with machine learning techniques (e.g., for demand forecasting or anomaly detection) to improve model performanceDegree in Electrical Engineering, Operations Research, Computer Science, or a related field; or equivalent experienceCompensation and BenefitsBenefitsAlong with competitive pay, as a full-time Tesla employee, you are eligible for the following benefits at day 1 of hire:Aetna PPO and HSA plans > 2 medical plan options with $0 payroll deductionFamily-building, fertility, adoption and surrogacy benefitsDental (including orthodontic coverage) and vision plans, both have options with a $0 paycheck contributionCompany Paid (Health Savings Account) HSA Contribution when enrolled in the High Deductible Aetna medical plan with HSAHealthcare and Dependent Care Flexible Spending Accounts (FSA)401(k) with employer match, Employee Stock Purchase Plans, and other financial benefitsCompany paid Basic Life, AD&D, short-term and long-term disability insuranceEmployee Assistance ProgramSick and Vacation time (Flex time for salary positions), and Paid HolidaysBack-up childcare and parenting support resourcesVoluntary benefits to include: critical illness, hospital indemnity, accident insurance, theft & legal services, and pet insuranceWeight Loss and Tobacco Cessation ProgramsTesla Babies programCommuter benefitsEmployee discounts and perks programExpected Compensation$124,000 - $330,000/annual salary + cash and stock awards + benefitsPay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. The total compensation package for this position may also include other elements dependent on the position offered. Details of participation in these benefit plans will be provided if an employee receives an offer of employment. #J-18808-Ljbffr

Created: 2025-09-21