Temporal Software Engineer Jobs in Dallas, TX (NOW HIRING)

Job Summary (List Format): Data Annotator & QA Reviewer – Autonomy & Robotics (Mining)
- Perform manual data annotation and quality assurance (QA) review for perception and VLA (Vision-Language-Action) data, including video, images, and multi-sensor machine data.
- Annotate mining site entities (e.g., roads, rock piles, vehicles, personnel, machinery) in both 2D and 3D data formats (LiDAR, radar, video).
- Track heavy equipment trajectories and operational states, including motion paths, articulation, bucket/blade actions, and velocity in challenging mining environments.
- Align and fuse data from various sensors (camera, LiDAR, GPS/GNSS, IMU, CAN bus, payload sensors) to maintain accurate spatial and temporal mapping.
- Decompose mining workflows into structured task sequences, labeling actions, operator/machine intent, causations, and outcomes for autonomous system training.
- Model and annotate causal relationships and site-specific triggers (e.g., environmental changes, equipment reactions) in mining operations.
- Tag and verify outcomes of machine actions, comparing expected vs. actual results (e.g., load success, hazard avoidance, maneuver outcomes).
- Conduct rigorous QA audits of labeled datasets, ensuring high accuracy, semantic consistency, and correct handling of mining-specific edge cases (dust, mud, night, glare, underground).
- Provide feedback and update labeling guidelines based on emerging annotation challenges and edge cases.
- Utilize various labeling platforms (CVAT, Labelbox, Scale AI, Supervisely, V7, Encord, etc.) for high-precision data annotation.
- Collaborate with internal and external teams to maintain data quality standards and continuously improve annotation processes.
Required Skills & Qualifications:
- 1+ years of experience in data annotation, labeling, or QA for computer vision, robotics, or autonomous systems.
- Experience with 3D spatial data (LiDAR, point clouds, depth maps, spatial trajectories, multi-camera feeds).
- Strong attention to detail, especially for complex spatial and environmental scenarios.
- Familiarity with mining operations, heavy equipment, and related safety/operational terminology.
- Technical aptitude with geospatial/sensor data formats (JSON, XML) and labeling tools.
- Ability to breakdown complex workflows into sequenced actions and label accordingly.
- Strong 3D spatial visualization and perception skills.
Desired (Nice to Have):
- Background in Mining Engineering, Robotics, Autonomous Vehicles, or related fields.
- Experience with autonomous haulage systems or industrial robotics VLA models.