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Salary: USD 150,000 - 200,000 / annual
ThinkLabs AI is building next-generation software for electric power system simulation, optimization, and analytics. The company combines rigorous power systems engineering, advanced numerical methods, optimization, high-performance computing, and modern AI/ML techniques to modernize power systems analysis and accelerate the energy transition.
You will lead the design and development of advanced power systems technologies and the high-performance computing foundation that runs them. This role combines deep power system domain expertise, mathematical and numerical rigor, and high-performance software engineering. You will design production-quality software in modern C++, with GPU acceleration (CUDA) as a first-class design goal.
Key Responsibilities:
- Design and implement transmission/distribution robust and scalable algorithms from the ground up, ensuring numerical correctness, consistency, and reproducibility across CPU and GPU implementations.
- Develop production-quality scientific computing software in modern C++ with emphasis on performance, memory efficiency, and maintainability.
- Lead the architecture and evolution of GPU-accelerated compute capabilities across the simulation and optimization platform.
- Design and optimize numerical workloads for modern GPU hardware, including memory management, workload batching, data movement, and asynchronous execution.
- Profile complex computational workloads, identify bottlenecks, and drive measurable performance improvements.
- Develop ML-assisted initialization and warm-start approaches for power systems applications; explore AI-assisted contingency screening and ranking.
- Architect extensible software infrastructure and APIs for transmission-system studies.
- Serve as a deep technical expert in power system mathematics, technologies, and performance engineering.
- Mentor engineers and researchers across power systems, HPC, and AI domains.
You will work closely with power systems specialists, numerical engineers, and AI engineers to translate computationally intensive algorithms into scalable production software and rethink how traditional power system analysis can evolve using modern software, HPC, and AI approaches.
Requirements:
- Master's degree in electrical engineering, applied mathematics, physics, computer science, or related technical field, with minimum 3 years of relevant working experience.
- Deep understanding and strong background in transmission and/or distribution system analysis and modeling.
- Familiarity with industry simulation tools such as PSS/E, PSLF, DSATools, OpenDSS, or ETAP.
- Strong professional experience developing high-performance or computational software in modern C++ (C++17 or later), including production-quality systems software.
- Hands-on experience with GPU programming and CUDA, including developing, profiling, and optimizing numerical workloads; solid understanding of GPU architecture, parallel computing, memory management, and asynchronous execution.
- Strong mathematical foundation in linear algebra, sparse matrix methods, and numerical methods, with clear understanding of numerical accuracy, floating-point behavior, and reproducibility.
- Experience implementing algorithms directly rather than only using commercial tools.
- Demonstrated ability to use profiling and benchmarking to identify bottlenecks and deliver measurable performance improvements.
- Working proficiency in Python and familiarity with Linux, Docker containers, modern build/test/deployment practices, and microservice environments.
- Experience applying AI/ML to physical systems or optimization problems; familiarity with machine learning and LLM tooling.
- Strong analytical, problem-solving, communication, and technical leadership skills; ability to collaborate effectively across engineering and domain-specialist teams in a remote environment.
Desired Qualifications:
- Ph.D. in electrical engineering, applied mathematics, physics, computer science, or related technical field, with minimum 5 years of relevant working experience.
- GPU-accelerated sparse direct power systems technologies; sparse matrix factorization and large-scale sparse linear system solving.
- GPU acceleration of optimization or scientific-computing workloads; multi-GPU or distributed high-performance computing; NVIDIA numerical libraries (cuSPARSE, cuBLAS, cuSOLVER) and GPU computing frameworks.
- Proven experience as a power systems engineer with expert knowledge of power system models, model validation and calibration, and steady-state, transient, and dynamic analysis.
- Experience shipping production scientific, engineering, simulation, or optimization software.
- Industry reputation, prior IEEE/CIGRE publications on practical power systems engineering topics, and participation in industry standards, working groups, or task forces.
