Staff ML Engineer, Generative Model Performance & Efficiency

📋 Description

• Analyze model architectures and identify bottlenecks in training and inference performance (memory
• Apply and develop quantization, pruning, distillation, and efficient attention techniques.
• Optimize model code for hardware accelerators (TPUs, GPUs) using compiler features and low-level
• Experiment with model partitioning and sharding (data, tensor, pipeline, expert) to improve
• Design and implement low-latency, high-throughput serving for generative models and optimize
• Build and maintain tools for performance analysis, profiling (e.g., xprof), and debugging ML models.

🎯 Requirements

• MS or PhD in Computer Science, ML, Robotics, or related field.
• 5+ years of experience with deep learning architectures (Transformers, Diffusion Models, MoEs) and
• Proficiency in JAX, Flax, and potentially TensorFlow/PyTorch.
• Experience with profiling tools (XProf, Perfetto, NVIDIA Nsight) to diagnose ML performance issues.
• Hands-on experience with quantization, pruning, distillation, and other model compression methods.
• Strong Python programming, with knowledge of C++ and software development best practices.

🎁 Benefits

• Salary range: $251,000 – $310,000 USD.
• Discretionary annual bonus program, equity incentives, and generous benefits.