Machine Learning Engineer

Degree in computer science, engineering, applied mathematics, or a related technical field, or equivalent industry experience building ML systems.
Strong experience writing and maintaining production-quality code
Strong proficiency in Python and experience with machine learning frameworks such as PyTorch or TensorFlow.
Solid understanding of ML training workflows, including dataset preparation, model evaluation, and performance diagnostics.
Experience with synthetic data generation, simulation tools, or 3D rendering environments such as Blender or Unity.

Identify opportunities to improve the synthetic data pipeline and deliver a meaningful enhancement that increases dataset quality, control, or scalability.
Propose and implement new data-generation or augmentation techniques based on assessment of model bottlenecks, training patterns, or failure modes.
Validate effectiveness through structured training experiments and benchmark results against existing approaches.
Partner with ML scientists and technical artists to translate visual intuition and ML needs into robust software.
Identify gaps in configuration, data realism, and augmentation strategy that impact performance.
Design and implement tooling to configure and control synthetic data generation at scale.
Run targeted training experiments to measure the impact of data approaches and guide future improvements.
Build internal tools that expose dataset properties, track changes, and help others reason about training inputs.

Ability to design, execute, and interpret training experiments to evaluate the impact of data and augmentation strategies.
Comfortable working in Linux-based development environments and with Docker-based workflows.
Experience with domain randomization, synthetic-to-real transfer, or sim-to-real techniques in robotics or computer vision.
Background in computer vision tasks such as object detection, segmentation, or 6-DoF pose estimation.
Experience working with cloud-based ML infrastructure.