Applied Research Engineer – Robotics Data & ML

Overview

We are looking for a hands-on Applied Research Engineer with a strong foundation in robotics, machine learning, and multi-sensor data processing to join our Research & Delivery team. This role is ideal for someone with 3–5 years of experience in applied ML, computer vision, or robotic systems who is eager to scale their impact by helping build robust, high-quality datasets that fuel modern perception, SLAM, and manipulation models.

You will collaborate closely with ML leads as well as data operations teams to design annotation strategies, contribute to small-scale model fine-tuning, and translate model needs into precise data specifications. This role is both technical and cross-functional—strong communication skills and a collaborative mindset are critical for success.

Key Responsibilities

ML-Aligned Data Development

• 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).

Model Support and Fine-Tuning

• 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.

QA and Annotation Workflow Design

• Contribute to quality control processes—build checklists, gold sets, and feedback loops that ensure consistent, scalable labeling outcomes.

Cross-Functional Communication

• 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.

Qualifications

• 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.

What Success Looks Like

• 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.

Compensation: $170,000 to $200,000 base salary + Equity