Senior AI Systems Performance Engineer

SambaNova is a pioneering company in the generative AI space, providing a full-stack platform optimized for various organizations. The role involves optimizing and scaling advanced foundation models, bridging deep learning and systems performance to achieve exceptional AI inference performance.

Responsibilities:

• Bring up and optimize cutting-edge foundation models (e.g., DeepSeek, Llama, Qwen, and others) on the SambaNova platform through the SambaNova software stack
• Profile and enhance model performance across compiler, runtime, and hardware layers to achieve SOTA throughput and latency
• Collaborate with machine learning, compiler, runtime, and hardware teams to deliver co-designed, high-performance AI applications
• Integrate the latest advances in model architecture, quantization, scheduling, and memory optimization from both academia and industry
• Develop robust, scalable, and efficient end-to-end inference solutions aligned with customer needs
• Identify performance bottlenecks and propose dataflow or scheduling optimizations for both single-node and distributed systems

Requirements:

• Bachelor's or higher degree in computer science, electrical engineering, or a related field (e.g., applied mathematics, physics, or statistics)
• 3+ years of experience in one or more of the following areas: Deep learning model development and performance optimization, Compiler, runtime, or kernel-level optimization, Software–hardware co-design or systems performance tuning
• Proficiency in Python or C++, with strong foundations in algorithms, data structures, and numerical computing
• Experience with at least one major ML framework — PyTorch, TensorFlow, or JAX
• Demonstrated ability to analyze and optimize performance in real-world ML pipelines
• Hands-on experience with LLM or multimodal model training and inference
• Background in large-scale distributed training, continuous batching, and high-throughput inference systems
• Familiarity with quantization, graph optimization, kernel fusion, and model partitioning
• Experience with frameworks such as DeepSpeed, Megatron, vLLM, or TensorRT
• Strong GPU programming skills (CUDA, Triton, or OpenCL); experience with cuDNN, cuBLAS, or similar libraries is a plus
• Knowledge of memory hierarchy optimization, caching, and scheduling for large-scale model execution
• Publication record or open-source contributions in ML systems or performance optimization is a plus