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Hybrid Lead Software Engineer - AI/LLM Physics Applications at Exowatt

Exowatt · Miami, United States Of America · Hybrid

Python

Docker Container Platforms

Kubernetes Container Platforms

AWS

MLOps

Software Engineer

Cloud

Apply Now
Exowatt is revolutionizing the energy landscape for the AI era with our groundbreaking P3 system that captures solar energy, stores it as heat, and generates electricity on demand. Founded in 2023 and backed by leading investors including Andreessen Horowitz, Sam Altman, and Felicis, we're committed to providing clean, modular, and scalable power that meets the rapidly growing demands of AI infrastructure. Our mission is to make sustainable renewable energy always available and almost free, enabling technological advancement while protecting our planet.

We are seeking an innovative Lead Software Engineer to pioneer the application of Large Language Models (LLMs) and AI to solve complex physics and engineering challenges in our renewable energy systems. This unique role combines cutting-edge AI technology with fundamental physics to optimize thermal dynamics, predict system behavior, and accelerate our engineering development cycles.

You will lead the development of AI-powered tools that help our engineers design better systems, predict performance under various conditions, and solve multiphysics problems that are critical to our generation, thermal energy storage and conversion technology. This is a rare opportunity to apply the latest advances in AI to real-world energy challenges that will power the future of computing.

This position is based in Miami, FL. If selected, relocation to Miami, FL is required. Exowatt provides relocation assistance.


Key Responsibilities
  • Develop AI/LLM-based models for complex thermodynamics, heat transfer, and fluid dynamics.
  • Implement AI to interpret, validate, and optimize physics simulations.
  • Ensure AI solutions adhere to thermodynamics principles.
  • Stay updated on advances in AI/physics-informed computing.
  • Collaborate externally and represent Exowatt in scientific forums.
  • Integrate AI frameworks with engineering simulation platforms (e.g., ANSYS).
  • Build scalable, cloud-based software architectures and real-time data pipelines.
  • Lead AI-driven digital twin and predictive analytics for system optimization and maintenance.



    • Required Qualifications
  • Bachelor's degree in Computer Science, Physics, Engineering, or related field; Master's or PhD preferred
  • 7+ years of software engineering experience with at least 3 years focused on AI/ML applications
  • Track record of building production AI systems that solve real-world problems
  • Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, JAX)
  • Strong proficiency with LLM frameworks and tools (Hugging Face, LangChain, OpenAI API, Anthropic Claude)
  • Solid understanding of physics fundamentals, particularly thermodynamics and heat transfer
  • Proficiency with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes)
  • Experience with MLOps practices and model deployment pipelines



    • Preferred Qualifications
  • Expertise in physics-informed neural networks (PINNs) or neural operators
  • Domain experience with renewable energy, thermal systems, or concentrated-solar-power (CSP)
  • PhD in Computational Physics, Applied Mathematics, or AI/ML (physics-focused)
  • Proficiency with CFD or FEA tools
  • Advanced mathematical tooling—symbolic math, automated theorem proving, optimization/constraint programming, or reinforcement learning
  • Demonstrated fine-tuning / prompt-engineering of domain-specific LLMs
  • Publication or patent record in AI for scientific computing



    • What We Offer:
    Competitive salary and equity options.
    Comprehensive benefits package, including health, dental, and retirement plans.
    A dynamic work environment that fosters creativity and innovation.
    Opportunities for professional growth and development in a rapidly evolving industry.
    Relocation assistance.

    Apply Now

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