Lightning AI
AI Performance Optimization Team Lead
Lightning AI, San Francisco, California, United States, 94199
AI Performance Optimization Team Lead
Lightning AI is reimagining how AI is built. After launching PyTorch Lightning in 2019, Lightning AI reshapes AI product development for commercial and academic use.
Responsibilities
Own technical direction of performance‑oriented model optimization at graph, kernel, and system levels.
Advance compiler technology by building optimization passes, graph transformations, and integration hooks across the Lightning stack.
Ensure optimizations are accessible through clean APIs, automated tooling, and integration with PyTorch Lightning and LitServe.
Design and implement profiling and debugging tools to analyze model execution, identify bottlenecks, and guide optimization strategies.
Collaborate with hardware vendors and ecosystem partners to ensure efficient workloads across NVIDIA, AMD, TPU, and specialized accelerators.
Contribute to open‑source projects, improve documentation, and support community adoption.
Engage with researchers and engineers to guide performance tuning and advocate for the Lightning stack.
Work cross‑functionally with product and engineering teams to align compiler and optimization improvements with broader product vision.
Qualifications
Strong expertise with deep learning frameworks such as PyTorch, JAX, or TensorFlow.
Hands‑on experience profiling models on hardware accelerators and identifying bottlenecks.
Experience with model optimization techniques (graph‑level optimizations, quantization, pruning, mixed precision, memory‑efficient training).
Deep understanding of compiler internals or performance‑critical software.
Experience with CUDA, Triton, or other GPU programming models for custom kernels.
Knowledge of distributed computing and parallelism strategies.
Proficiency in API design, robust tooling, testing, CI/CD for performance‑sensitive systems.
Track record contributing to open‑source projects in AI, scientific computing, or compilers.
Excellent collaboration and communication skills across research, engineering and external contributors.
Bachelor's degree in Computer Science, Engineering, or related field; Master’s or PhD highly preferred.
Benefits and Perks
Competitive base salary $225k–$275k, stock options with 25% one‑year cliff and monthly vesting.
Medical, dental, vision, life, AD&D, flexible PTO and paid family leave.
$500 monthly meal reimbursement, $500 one‑time home office stipend, $1,000 annual learning stipend.
100% Citibike membership (NYC only), $45/month gym membership.
Additional medical and mental health services.
EEO Statement At Lightning AI, we are committed to fostering an inclusive and diverse workplace. We believe that diverse teams drive innovation and create better products. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic. We are dedicated to building a culture where everyone can thrive and contribute to their fullest potential.
Salary range $225k–$275k. Hybrid, San Francisco office, 2 days in‑office per week.
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Responsibilities
Own technical direction of performance‑oriented model optimization at graph, kernel, and system levels.
Advance compiler technology by building optimization passes, graph transformations, and integration hooks across the Lightning stack.
Ensure optimizations are accessible through clean APIs, automated tooling, and integration with PyTorch Lightning and LitServe.
Design and implement profiling and debugging tools to analyze model execution, identify bottlenecks, and guide optimization strategies.
Collaborate with hardware vendors and ecosystem partners to ensure efficient workloads across NVIDIA, AMD, TPU, and specialized accelerators.
Contribute to open‑source projects, improve documentation, and support community adoption.
Engage with researchers and engineers to guide performance tuning and advocate for the Lightning stack.
Work cross‑functionally with product and engineering teams to align compiler and optimization improvements with broader product vision.
Qualifications
Strong expertise with deep learning frameworks such as PyTorch, JAX, or TensorFlow.
Hands‑on experience profiling models on hardware accelerators and identifying bottlenecks.
Experience with model optimization techniques (graph‑level optimizations, quantization, pruning, mixed precision, memory‑efficient training).
Deep understanding of compiler internals or performance‑critical software.
Experience with CUDA, Triton, or other GPU programming models for custom kernels.
Knowledge of distributed computing and parallelism strategies.
Proficiency in API design, robust tooling, testing, CI/CD for performance‑sensitive systems.
Track record contributing to open‑source projects in AI, scientific computing, or compilers.
Excellent collaboration and communication skills across research, engineering and external contributors.
Bachelor's degree in Computer Science, Engineering, or related field; Master’s or PhD highly preferred.
Benefits and Perks
Competitive base salary $225k–$275k, stock options with 25% one‑year cliff and monthly vesting.
Medical, dental, vision, life, AD&D, flexible PTO and paid family leave.
$500 monthly meal reimbursement, $500 one‑time home office stipend, $1,000 annual learning stipend.
100% Citibike membership (NYC only), $45/month gym membership.
Additional medical and mental health services.
EEO Statement At Lightning AI, we are committed to fostering an inclusive and diverse workplace. We believe that diverse teams drive innovation and create better products. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic. We are dedicated to building a culture where everyone can thrive and contribute to their fullest potential.
Salary range $225k–$275k. Hybrid, San Francisco office, 2 days in‑office per week.
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