Parallel Domain

Data Engineer

• Own data ingestion: Build reliable pipelines to normalize and validate customer and synthetic data.
• Define data standards: Create schemas, validation checks, and quality metrics for Replica datasets.
• Build curation tooling: Implement tools for dataset filtering, versioning, and annotation support.
• Enable ML workflows: Generate high-quality data feeds for training and evaluation across ML models.

Machine Learning Data Engineer, Replica Pipelines

Machine Learning Engineer

• Develop innovative ML models: Design and implement video-to-video diffusion models and efficient 4D feed-forward reconstruction methods.
• Improve scalability and performance: Optimize models and pipelines to support large-scale Replica environments.
• Productionize research: Ship robust, documented ML components integrated with Replica tooling.
• Collaborate cross-functionally: Engage with simulation, rendering, and infrastructure engineers to deliver end-to-end solutions.

Senior Machine Learning Engineer, Diffusion & Reconstruction

• Build robust SfM and pose pipelines: Improve accuracy, speed, and reliability of pose estimation components.
• Develop geometric vision models: Design and implement learned modules for camera calibration and pose refinement.
• Integrate ML and classical methods: Combine geometric algorithms with modern ML tools for practical performance.
• Collaborate on data and evaluation: Partner with ML engineers to curate datasets and define metrics for success.

Parallel Domain

Machine Learning Data Engineer, Replica Pipelines

Machine Learning Data Engineer, Replica Pipelines

Senior Machine Learning Engineer, Diffusion & Reconstruction

Senior Machine Learning Engineer, Diffusion & Reconstruction

Senior Machine Learning Engineer, Pose Estimation

Senior Machine Learning Engineer, Pose Estimation