You will design and maintain the data, training, and production pipelines that support machine learning and autonomy models for ROZOR robots, dataset generation, annotation systems, simulation training, and model deployment at scale.
Responsibilities Construct and maintain pipelines for ingesting, labeling, storing, and processing robot datasets.
Create annotation systems for perception and autonomy work.
Manage training pipelines, experiment tracking, and evaluation frameworks.
Implement MLOps practices for continuous training, testing, and deployment of ML models.
Integrate cloud infrastructure for scalable and distributed computation.
Build simulation systems for synthetic data and reinforcement learning.
Collaborate with autonomy, perception, and embedded teams on data requirements.
Ensure reproducibility, versioning,
and traceability of datasets and ML models.
Optimize inference for edge deployment on Jetson and ARM-based systems.
Monitor model performance and data drift across deployed robots.
What you bring Bachelor's/Master's in Computer Science, AI, or related field.
2+ years in ML infrastructure, MLOps, or data engineering.
Python proficiency and ML tooling experience.
Cloud platform and distributed-systems familiarity.
CI/CD, Docker, and Kubernetes knowledge.
Data workflow design experience.
Model training and deployment understanding.
Robust debugging and scalability focus.