Senior Machine Learning Engineer - Physical AI and Synthetic Data Generation

NVIDIA•Us, CA

17h

About The Position

We are looking for outstanding Machine Learning Engineers to join our Physical AI teams! As the pioneers of the GPU—the visual cortex of modern computing—we are building the foundation for the next wave of AI that interacts with the physical world. This role is at the forefront of Physical AI, developing sophisticated generative pipelines to build high-fidelity synthetic datasets. It leverages state-of-the-art multimodal models and diffusion techniques to simulate complex physical environments, ensuring our AI agents are trained on the most diverse and rigorous data possible. We work closely with various users of synthetic datasets, including policy models. It extends an opportunity to contribute to the technology that will drive the cars of the future!

Requirements

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 among others.
Various hands on experience with workflow management and database to facilitate large scale training and data generation.
Strong analytical and mathematical skills to bridge the gap between data-driven approaches and physical world constraints.
A collaborative outlook with outstanding communication skills, thriving in a tightly-knit team environment.
Experience in assessing the impact of synthetic data on model performance through metrics and systematic validation.

Nice To Haves

Experience with computer/GPU architecture to improve the performance during inference/training.
Familiarity with simulation platforms and deep understanding of 3D sensor modalities (Camera, Multi cameras, Lidar, Radar).
Experience with open source software.
Strong skills to optimize code efficiency is a huge plus.

Responsibilities

Architect Generative Pipelines: Develop and implement advanced image and video generation/editing/reasoning models to produce high-fidelity synthetic data for Physical AI applications.
Multimodal Development: Build and fine-tune large-scale models, including VLMs, MLLMs, Generation models, applying transformer, auto-regressive and diffusion-based architectures.
Controllable Synthesis: Apply and evolve user controls during data generation to ensure precise environmental and structural control over generated data.
Detailed Validation: Establish a strong mentality for KPI evaluation and validation to ensure the quality and physical accuracy of the synthetic releases.
Automated Quality Assurance: Build and test automated data QA pipeline using a mix of well known classical computer vision algorithms, and VLMs.
SOTA Data Engineering: Lead the generation of massive training datasets using various state-of-the-art tools and synthetic data mining techniques.
Contribute to the full lifecycle of ML software, including performance optimization, testing, and high-quality documentation.