AI Systems Performance Engineer, SambaNova Systems

• This is an ideal opportunity for a new graduate who is passionate about understanding how AI models execute on real hardware and wants to help build the next generation of high-performance AI systems
• Bachelor's or Master's degree in computer science, electrical engineering, computer engineering, or a related technical field (e.g., applied mathematics, physics, or statistics), completed or expected before the start date
• Strong programming skills in Python, C++, or a similar programming language
• Solid foundations in algorithms, data structures, computer architecture, operating systems, or parallel computing
• Familiarity with deep learning and at least one major ML framework, such as PyTorch, TensorFlow, or JAX
• Strong analytical and problem-solving skills, with an interest in understanding and optimizing system performance
• Ability and enthusiasm to learn across machine learning, software systems, and hardware
• We value strong technical fundamentals, curiosity, and the ability to learn quickly. Prior production experience with large-scale AI systems is not required

Desirable

• We are seeking a talented and highly motivated AI Systems Performance Engineer to bring up and optimize state-of-the-art foundation models on SambaNova's reconfigurable dataflow platform
• You'll work hands-on with advanced AI models — such as DeepSeek, GLM, Kimi, GPT OSS, Llama, Qwen, and other frontier architectures — and learn how modern AI systems achieve high throughput, low latency, and efficient large-scale inference
• In this role, you'll work at the intersection of machine learning and computer systems, collaborating with engineers across model, compiler, runtime, and hardware teams
• Bring up cutting-edge foundation models, including LLMs and multimodal models, on the SambaNova platform through the SambaNova software stack
• Analyze and profile model execution to identify performance bottlenecks across model, compiler, runtime, and hardware layers
• Optimize AI workloads for throughput, latency, memory efficiency, and scalability
• Collaborate with machine learning, compiler, runtime, and hardware engineers to develop high-performance AI applications
• Explore and integrate new techniques in model architecture, quantization, scheduling, caching, and memory optimization
• Develop tools, benchmarks, and performance analysis methodologies for large-scale AI inference
• Investigate new model architectures and translate research advances into efficient implementations on production AI systems
• Contribute ideas for dataflow, scheduling, and system optimizations for both single-node and distributed inference