Sr. Software Engineer, Energy Optimization

What to Expect

Opticaster’s mission is to accelerate the world’s transition to sustainable energy by optimizing how batteries are charged and discharged to maximize value for customers and the grid. Our advanced algorithms determine the best times to store and use energy, considering electricity prices, solar generation, and grid conditions. These optimizations power Tesla’s energy products, including Megapacks, Powerwalls, Virtual Power Plants, and microgrids. With a global fleet of over 500,000 residential systems, our software operates at scale, requiring robust and efficient solutions. Opticaster also plays a key role in supporting grid stability through programs like demand response and by integrating with Autobidder to actively participate in energy markets.

Opticaster is a customer-facing product: the software you build directly shapes how hundreds of thousands of homes and energy sites store, use, and export energy every day. As a Software Engineer on Opticaster, you will build the production systems and internal tooling that let algorithm changes ship safely to a global energy fleet. Part of the role is improving developer and research workflows with AI tools—evaluation harnesses, failure analysis, experiment tooling, and automation around simulation, diagnostics, and release. You will also work on the software that surrounds Opticaster in production: Python services, firmware/Site Manager integration points, simulation/benchmarking platforms, cloud data pipelines, CI/CD, and observability. You will partner closely with algorithm engineers to develop, evaluate, integrate, and operate Opticaster at scale.

What You’ll Do

• Implement and ship production changes in the Opticaster codebase, taking features and fixes from development through validation and release to the customer fleet
• Build and improve internal tooling that accelerates Opticaster development and operations, including evaluation workflows, regression detection, experiment analysis, diagnostics, and automation
• Apply AI tools to real team workflows—assisted analysis of simulation/fleet results, smarter evaluation loops, and automation that reduces repetitive debugging and review—with measurable productivity and quality gains
• Own and evolve the software platforms around Opticaster: simulation/benchmarking systems, cloud evaluation pipelines, monitoring/alerting, and release automation
• Design and maintain production integration points with Tesla Energy firmware and Site Manager—interfaces, signals, parsers/inputs, configuration, and version compatibility
• Strengthen reliability and operability with monitoring, alerting, dashboards, and tooling that explain field or simulation behavior
• Improve CI/CD and release automation so changes can be validated and shipped safely
• Identify and resolve performance bottlenecks (CPU, memory, latency, runtime) in services, pipelines, evaluation workloads, and constrained on-device/runtime paths
• Partner with algorithm, firmware, and product engineers to turn energy-domain requirements into production-ready software, interfaces, and tooling

What You’ll Bring

• Degree in Computer Science, Engineering, or equivalent experience
• Strong proficiency in Python and Linux
• Experience building and shipping production software and/or internal developer tooling—services, pipelines, evaluation systems, or automation with real users
• Experience using AI tools to build high-leverage engineering tooling and workflows
• Experience with cloud and data technologies such as AWS, Spark, Airflow/Argo (or equivalent orchestration), Docker/Kubernetes, and CI/CD
• Experience owning complex production software through design, implementation, debugging, and operation—where correctness, performance, and maintainability matter
• Excellent communication skills and the ability to collaborate across algorithm, firmware, and product partners
• Experience with another production language (e.g. C++, Go, Rust, Java, or C#) is a plus
• Experience with production systems that have real integration, reliability, or performance constraints is a plus
• Experience with energy systems, on-device or firmware-adjacent environments, or machine learning is a plus