Job Title

Position: & Simulation Tirage Engineer Work Location: Work from Office / Remote Type: Full-time Shift: Flexible to work in shifts, including night shifts on client request Communication Skills Requirement: Minimum CEFR B2 level of English Technical Skills Requirement: LiDAR L3 About the Role (Position Summary) This role requires a strong combination of technical expertise in AV sensor data (especially LiDAR and camera), meticulous attention to annotation quality, and hands-on experience in simulation data triage. The ideal candidate will work on identifying errors, inconsistencies, and gaps in annotated datasets and simulation scenes, supporting the retraining and validation cycles of autonomous vehicle (AV) systems. Education Qualifications Bachelor's degree in Engineering, Computer Applications, Data Science, or related technical discipline. Experience 3–7 years of experience in annotation, simulation validation, or auditing roles with increasing leadership responsibility. Strong background in LiDAR and sensor data (2D/3D bounding boxes, polygons, cuboids, segmentation). Experience in simulation scenario review and tirage pipelines (e.g., triaging data for model retraining). Prior exposure to client interaction and cross-functional coordination (e.g., with engineering and QA teams). Hands-on experience working in tool-based labeling pipelines and simulation-driven testing environments. Experience working with annotation tools and QA checklists based on project-specific SOPs. Capacity of data analysis towards identifying consistent gaps Desired Qualifications and Experience (if any) Auditor-Specific Soft Skills Exceptional attention to detail: Ability to notice minute annotation errors, shape irregularities, and alignment mismatches. Strong visual-spatial reasoning: Capability to interpret 3D point clouds and assess object depth, perspective, occlusion, and context. Critical thinking: Skill in evaluating edge cases and understanding when annotations fail to represent real-world driving scenarios accurately. Strong ability of pattern recognition Annotation guideline interpretation: Ability to deeply understand, question, and apply detailed project-specific SOPs and edge-case rules. Bias awareness: Understand and flag biases in annotation or scene interpretation (e.g., object labeling errors due to weather, occlusion, or human assumptions). Clear written communication: Document audit findings, feedback, and edge cases in structured and unambiguous formats. Consistency under repetition: Maintain high focus and precision while reviewing large datasets with repetitive structures. Curiosity and domain awareness: Stay engaged with AV trends, annotation standards (e.g., KITTI, nuScenes), and common model failure patterns. Collaboration mindset: Willingness to work closely with fellow auditors, QA teams, and simulation engineers to improve annotation and tirage workflows. Technical Skill Requirements Annotation Auditing & Data QA Annotate and/or Audit 2D/3D sensor data annotations to ensure they meet the project’s accuracy, completeness, and consistency requirements. Identify common error types and provide actionable feedback. Simulation Tirage Evaluate simulation data for complexity, relevance, and anomalies. Tag and classify scenes based on criticality for model training or validation pipelines. Clear understanding of road sign and rules from a driver's perspective with capacity of (given) situation analysis Rules of the Road Expertise: Familiarity with standard traffic behavior, signage, signaling, right-of-way principles, and vehicle interactions under typical U.S. driving conditions. MUTCD Knowledge: Working knowledge of the Manual on Uniform Traffic Control Devices (MUTCD), especially as it applies to lane markings, signage, and signal interpretation across varied roadway types. Passenger Comfort Sensitivity: Ability to assess scenarios from the perspective of a rider, identifying discomfort or unsafe behaviors that may not violate technical rules but still degrade the end-user experience. Driver Duty of Care Perspective: Ability to evaluate edge cases and ambiguous situations through the lens of a responsible human driver, balancing legality with caution and accountability in mixed-traffic environments. Tool & Workflow Expertise Operate advanced tools such as Labelbox, Scale AI, Supervisely, CVAT, CARLA, or in-house labeling systems. Suggest refinements in annotation and tirage flows to reduce ambiguity and improve cycle efficiency. Documentation & Reporting Maintain issue logs and generate structured audit reports. Provide detailed scene-level comments to support iterative data improvements. Responsibilities (not limited to) Conduct systematic reviews of AV sensor annotations using defined QA guidelines. Perform simulation scene triage to identify edge-case scenarios and misclassified outcomes. Tag errors, provide audit-level feedback, and ensure data is looped back for correction or retraining. Collaborate with QA reviewers, annotation operators, and simulation engineers. Contribute to refinement of SOPs and visual reference materials based on audit insights. Stay current with industry annotation standards and simulation evaluation protocols.