Job Description
- We are seeking a highly analytical and detail-oriented Associate – VLA Reviewer to
support advanced data review workflows for a leading autonomous vehicle
project.
- This role is focused on reviewing complex Vision-Language Action
annotation outputs, ensuring high-quality interpretation of dynamic
scenes, agent behavior, object interactions, spatial relationships, and
action sequences. The successful candidate will bring hands-on experience
in AV data annotation, VLA-related projects, and production-level review
workflows.
- This is an excellent opportunity for a candidate who thrives in
quality-focused AI data environments and is comfortable working with
complex visual and language-based annotation tasks.
Key Responsibilities
As
an Associate – VLA Reviewer, you will be responsible for:
- Reviewing autonomous vehicle data annotation outputs to ensure
quality, consistency, and alignment with project guidelines.
- Interpreting complex driving scenes involving multiple agents,
objects, interactions, and environmental context.
- Assessing agent actions, intent, object relationships, spatial
reasoning, and task sequences.
- Supporting Vision-Language Action workflows, including
vision-language alignment, action grounding, temporal understanding, and
contextual scene interpretation.
- Identifying and resolving ambiguous or edge-case annotation
scenarios using sound judgment and guideline interpretation.
- Applying annotation standards consistently across production review
tasks.
- Participating in calibration sessions, QA discussions, and feedback
loops to support ongoing quality improvement.
- Providing clear, structured, and actionable feedback to annotators
and project stakeholders.
- Escalating unclear guidelines, tooling issues, or recurring quality
gaps to project leads as appropriate.
Qualifications
Education
Requirements
- Diploma or higher qualification in a relevant field such as:
- Computer Science
- Information Technology
- Engineering (Electrical, Computer, Geospatial, or related)
- Data Science
- Geospatial Studies
- Or equivalent technical discipline
Required Experience
The
ideal candidate will have:
- Minimum 3 years of experience in autonomous vehicle data
annotation.
- Minimum 1 year of experience working on Vision-Language Action
projects.
- Experience working with complex scene understanding tasks,
including:
- Object interactions
- Agent behavior
- Spatial reasoning
- Action prediction
- Intent interpretation
- Task-sequence analysis
- Experience in review workflows, annotation guideline interpretation,
and edge-case handling within production environments.
Required Skills
We
are looking for candidates who can demonstrate:
- Strong understanding of VLA concepts, including vision-language
alignment, action grounding, temporal understanding, and contextual scene
interpretation.
- Ability to analyze dynamic environments and accurately label or
review agent actions, intent, object relationships, and task sequences.
- Excellent attention to detail and strong decision-making skills in
ambiguous annotation scenarios.
- Ability to quickly learn project-specific guidelines, tools,
workflows, and quality standards with minimal supervision.
- Strong communication and collaboration skills to contribute
effectively to calibration sessions, QA discussions, and reviewer feedback
loops.
- A disciplined and quality-driven approach to data review and
annotation accuracy.
Candidate Profile
- The successful candidate will be structured, observant, and
comfortable working with complex visual data. They will be able to balance
accuracy with productivity, apply detailed guidelines consistently, and
contribute to high-quality AI training data for autonomous vehicle
systems.
How to Apply
