Applied Research Engineer-Robotics Data & ML

Summary

Join Turing's Research & Delivery team as an Applied Research Engineer, contributing to the development of high-quality datasets for robotics and machine learning. Collaborate with ML leads and data operations teams to design annotation strategies, fine-tune models, and translate model needs into data specifications. This role requires 3–5 years of experience in applied ML, computer vision, or robotic systems and a strong foundation in robotics, machine learning, and multi-sensor data processing. You will be responsible for ML-aligned data development, model support and fine-tuning, QA and annotation workflow design, and cross-functional communication. Success involves creating well-documented annotation schemas, collaborating effectively, building consistent QA workflows, and driving improvements in dataset quality and model performance. The position offers a competitive salary and equity.

Requirements

• 3–5 years of hands-on experience in robotics, applied ML, or computer vision , ideally with some exposure to real-world sensor data or annotation workflows
• Strong understanding of robotics concepts such as perception pipelines , SLAM , or sensor fusion
• Familiarity with basic ML training and evaluation , particularly for computer vision or multi-modal data tasks
• Ability to read and synthesize ML research papers relevant to robotics
• Experience with tools such as ROS, CVAT, Roboflow , or custom labeling platforms
• Some exposure to model fine-tuning (e.g., with PyTorch, TensorFlow, or Hugging Face)
• Excellent written and verbal communication skills —comfortable translating technical needs across disciplines

Responsibilities

• Help define and evolve labeling schemas for robotic perception tasks, including: 2D/3D detection and segmentation, Grasp and manipulation point annotations, Scene affordances and human-robot interaction, Sensor fusion (e.g., aligning RGB + LiDAR + IMU)
• Align annotation strategies with key robotics benchmarks and downstream model use cases (e.g., RL, imitation learning, vision-based control)
• Under the guidance of a senior engineer, fine-tune and evaluate small ML models (e.g., lightweight vision or language models) for targeted robotics tasks
• Perform basic experiments to assess data effectiveness and model improvement
• Contribute to quality control processes—build checklists, gold sets, and feedback loops that ensure consistent, scalable labeling outcomes
• Collaborate with ML, robotics, and data labeling teams to turn model and benchmark requirements into clear, actionable data specs
• Write clear documentation and present technical updates to collaborators and stakeholders

Preferred Qualifications

• Creating well-documented, technically sound, annotation schemas that support learning, and generalization in robotic tasks
• Having clear, constructive, collaboration across ML, and data teams
• Building consistent QA workflows and reproducible data practices
• Driving measurable improvements in dataset quality and model performance
• Demonstrating initiative as well as growth in both ML modeling and data design responsibilities under mentorship of the greater team

Benefits

• Competitive compensation
• Flexible working hours
• Full-time remote opportunity