Senior AI Systems Performance Engineer, SambaNova Systems

• 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

Desirable

• We are seeking a talented and driven ML performance engineer to optimize and scale state-of-the-art foundation models on SambaNova's reconfigurable dataflow platform
• You'll work hands-on with some of the most advanced models in the world — such as DeepSeek R1, GPT OSS, and other frontier architectures — to push the limits of throughput, latency, and efficiency
• In this role, you'll bridge the gap between deep learning and systems performance, collaborating across compiler, runtime, and hardware layers to deliver world-record performance for large-scale AI inference
• 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