Software Engineer, ML Performance
Serve RoboticsFull Time
Senior (5 to 8 years), Expert & Leadership (9+ years)
The ideal candidate will have 3-5 years of hands-on experience in robotics, applied ML, or computer vision, with exposure to real-world sensor data or annotation workflows. A strong understanding of robotics concepts like perception pipelines, SLAM, or sensor fusion is necessary. Familiarity with basic ML training and evaluation, especially for computer vision or multi-modal data tasks, is required. The role also necessitates the ability to read and synthesize ML research papers relevant to robotics, experience with tools such as ROS, CVAT, Roboflow, or custom labeling platforms, and some exposure to model fine-tuning using frameworks like PyTorch, TensorFlow, or Hugging Face. Excellent written and verbal communication skills are essential for translating technical needs across disciplines.
The Applied Research Engineer will define and evolve labeling schemas for robotic perception tasks, including 2D/3D detection, segmentation, grasp and manipulation point annotations, scene affordances, human-robot interaction, and sensor fusion. They will align annotation strategies with key robotics benchmarks and downstream model use cases like RL, imitation learning, and vision-based control. The role involves fine-tuning and evaluating small ML models for targeted robotics tasks under senior guidance and performing basic experiments to assess data effectiveness. Additionally, the engineer will contribute to quality control processes by building checklists, gold sets, and feedback loops, and collaborate with ML, robotics, and data labeling teams to translate model and benchmark requirements into clear data specifications. This includes writing clear documentation and presenting technical updates to collaborators and stakeholders.
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