AI Systems Performance Engineer - New Graduate

The era of pervasive AI has arrived. In this era, organizations will use generative AI to unlock hidden value in their data, accelerate processes, reduce costs, drive efficiency and innovation to fundamentally transform their businesses and operations at scale. SambaNova Suite™ is the first full-stack, generative AI platform, from chip to model, optimized for enterprise and government organizations.

Powered by the intelligent SN40L chip, the SambaNova Suite is a fully integrated platform, delivered on-premises or in the cloud, combined with state-of-the-art open-source models that can be easily and securely fine-tuned using customer data for greater accuracy. Once adapted with customer data, customers retain model ownership in perpetuity, so they can turn generative AI into one of their most valuable assets.

About The Role

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.

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.

Responsibilities

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.

Qualifications

, 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.

Preferred Qualifications

Coursework, research, internship, or project experience in machine learning systems, computer architecture, compilers, distributed systems, or high-performance computing. Hands-on experience with LLMs, multimodal models, or transformer architectures. Familiarity with model inference, KV cache, batching, quantization, or distributed execution.

Experience with GPU or accelerator programming using CUDA, Triton, OpenCL, or similar technologies. Familiarity with frameworks such as vLLM, DeepSpeed, Megatron, or TensorRT. Understanding of memory hierarchy, caching, parallelism, or scheduling.