Senior Machine Learning Engineer - Physical AI and Synthetic Data Generation and Evaluation - nvidia

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 reasoning modules 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. In particular, we will build advanced quality assurance technology to validate generated data outputs. 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!

What you’ll be doing:

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. These models will take both visual and structured inputs (such as world model representations), and generate data and analyze consistency between scenarios intended by users and the generated data.

Controllable Synthesis: Apply and evolve user controls during data generation to ensure precise environmental and structural control over generated data.

Automated Quality Assurance for Sensor Data and Ego Policy: Build and test automated data QA pipeline using MLLMs and a mix of well known classical algorithms. In particular, build new capabilities to judge the quality of behavioral policies to ensure high quality data delivery for VLA.

Detailed Validation: Establish a strong mentality for KPI evaluation and validation to ensure the quality and physical accuracy of the synthetic releases. Establish a benchmark dataset. Design and validate KPI metric designs.

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.

What we need to see:

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.