Senior Machine Learning Engineer

Sourced from simplify.jobs

NVIDIA is seeking a Senior Machine Learning Engineer to join its Physical AI teams, focusing on building generative pipelines for high-fidelity synthetic data. The role involves developing and fine-tuning large-scale multimodal and diffusion-based models for image/video generation, with applications in autonomous vehicle simulation and policy learning. You will build automated QA systems, validate data quality through rigorous KPIs, and contribute to large-scale synthetic data generation.

• Architect generative pipelines for image/video generation, editing, and reasoning to produce high-fidelity synthetic data for Physical AI applications.
• Build and fine-tune large-scale multimodal models, including VLMs, MLLMs, and generation models, using transformer, auto-regressive, and diffusion-based architectures.
• Apply and evolve user controls for controllable synthesis to ensure precise environmental and structural control over generated data.
• Build and test automated data QA pipelines for sensor data and ego policy using MLLMs and classical algorithms.
• Establish KPI evaluation, validation, and benchmark datasets to ensure the quality and physical accuracy of synthetic releases.
• Lead massive training dataset generation using state-of-the-art tools and synthetic data mining techniques.
• Contribute to the full lifecycle of ML software, including performance optimization, testing, and documentation.

• BS, MS, or PhD in Computer Science, Computer Graphics, Robotics, or a related field (or equivalent experience).
• 12+ years of experience in ML software development.
• Deep technical knowledge of image/video synthesis, including diffusion models and state-of-the-art multimodal methods.
• Strong hands-on skills in major DNN libraries and computer languages, including Python.
• Experience with workflow management and databases for large-scale training and data generation.
• Strong analytical and mathematical skills to bridge data-driven approaches and physical world constraints.
• Collaborative outlook with outstanding communication skills.
• Experience assessing the impact of synthetic data on model performance through metrics and systematic validation.

• Experience with computer/GPU architecture to improve inference/training performance.
• Familiarity with simulation platforms and deep understanding of 3D sensor modalities (Camera, Multi cameras, Lidar, Radar).
• Experience with open source software.