We are now looking for a DL Algorithms Engineer! We are seeking a highly skilled Deep Learning Algorithms Engineer with hands-on experience optimizing and deploying Large Language Models (LLMs), Vision-Language Models (VLMs), and World Foundation Models (WFMs) in production environments. In this role, you will focus on optimizing and deploying deep learning models for efficient and fast inference across diverse GPU platforms, particularly for physical AI and generative AI applications. You will collaborate with research scientists, software engineers, and hardware specialists to bring cutting-edge AI models from prototype to production.

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What you will be doing:

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• Optimize deep learning models for low-latency, high-throughput inference, with a focus on LLMs, VLMs, diffusion models, and World Foundation Models (WFMs) designed for physical AI applications.
• Convert, deploy, and optimize models for efficient inference using frameworks such as TensorRT, TensorRT-LLM, vLLM, and SGLang.
• Understand, analyze, profile, and optimize performance of deep learning and physical AI workloads on state-of-the-art NVIDIA GPU hardware and software platforms
• Implement and refine components and algorithms for efficient serving of LLMs, VLMs, and WFMs at datacenter scale, leveraging technologies like Dynamo.
• Collaborate with research scientists, software engineers, and hardware specialists to ensure seamless integration of cutting-edge AI models from training to deployment
• Contribute to the development of automation and tooling for NVIDIA Inference Microservices (NIMs) and inference optimization, including creating automated benchmarks to track performance regressions

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What we want to see:

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• Master’s or PhD in Computer Science, Electrical Engineering, Computer Engineering, or a related field (or equivalent experience).
• Experience in deep learning, applied machine learning, or physical AI development.
• Strong foundation in deep learning algorithms, including hands-on experience with LLMs, VLMs, and multimodal generative models such as World Foundation Models.
• Deep understanding of transformer architectures, attention mechanisms, and inference bottlenecks.
• Proficient in building, optimizing, and deploying models using PyTorch or TensorFlow in production-grade environments.
• Solid programming skills in Python and C++.
• Experience with model quantization and modern inference optimization techniques (e.g., KV cache, in-flight batching, parallelization mapping).
• Strong fundamentals in GPU performance analysis and profiling tools (e.g., Nsight, nsys profiling).
• Familiarity with serving models using Triton Inference Server and PyTriton via Docker.

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Ways to stand out from the crowd:

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• Proven experience deploying LLMs, VLMs, diffusion models, or World Foundation Models (WFMs) at scale in real-world applications, especially for robotics or autonomous vehicles.
• Hands-on experience with model optimization and serving frameworks, such as: TensorRT, TensorRT-LLM, vLLM, SGLang, and ONNX.
• Direct experience with NVIDIA Cosmos, Isaac Sim, Isaac Lab, or Omniverse platforms for synthetic data generation and physical AI simulation.
• Experience with data curation pipelines and tools like NVIDIA NeMo Curator for large-scale video data processing and model post-training.
• Deep understanding of distributed systems for large-scale model inference and serving.

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