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

About the role

We are seeking Machine Learning Engineers to join our Physical AI teams. This role involves pioneering the GPU technology and building the foundation for the next wave of AI that interacts with the physical world. Key responsibilities include developing and implementing advanced image and video generation/editing/reasoning models, building and fine-tuning large-scale multimodal models, and establishing a strong mentality for KPI evaluation and validation.

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.
• 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.

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.
• Variety of hands-on experience with workflow management and database to facilitate large-scale training and data generation.
• Strong skills to optimize code efficiency is a huge plus.
• Strong analytical and mathematical skills to bridge the gap between data-driven approaches and physical world constraints.
• 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.

Ways to stand out from the crowd

• 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.

Qualifications

• 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.
• Variety of hands-on experience with workflow management and database to facilitate large-scale training and data generation.
• Strong skills to optimize code efficiency is a huge plus.
• Strong analytical and mathematical skills to bridge the gap between data-driven approaches and physical world constraints.
• 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.

Skills

• 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.

Benefits

• Base salary range: $224,000 - $356,500 for Level 5, and $272,000 - $431,250 for Level 6.
• Eligibility for equity and benefits.

Pay

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions.

Schedule

Full-time position.