Role Overview
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We are hiring a Robotics Data Pipeline & QA Engineer to build end-to-end infrastructures that move robotics video, sensor data, annotation output, and review results reliably through labeling workflows. You will combine software engineering, data pipeline design, robotics context, and automation-driven QA systems to ensure the highest-quality data is produced at scale.
Your work ensures robotics teams can collect, label, and validate thousands of hours of data per week with confidence and cost-efficiency.
Your Impact
- Build and optimize ingestion pipelines for robotics video, synchronized metadata, sensor logs, and derived annotations.
- Architect scalable labeling workflows that maintain ID integrity, time alignment, and version control across large datasets.
- Implement automated QA flows using heuristics, statistics, and LLM-based validation to reduce manual QA burden.
- Create dynamic trust scoring systems that ramp-down review percentage as contributors prove consistent quality.
- Track data progression across ingestion → labeling → review → acceptance → downstream consumption.
- Build monitoring systems for throughput, failure rates, accuracy, contributor performance, and cost impact.
- Identify robotic-specific annotation edge cases and translate them into codified criteria and QA logic.
- Collaborate with internal Platform, Infra, and ML teams to integrate tooling end-to-end.
You’re a Fit If You:
- Have hands-on experience with robotics systems, perception stacks, simulation, or structured robotics datasets.
- Can translate robotics data failure modes into measurable quality gates.
- Understand tradeoffs between human-in-loop QA vs automated review.
- Have experience designing pipelines that handle large media workloads (video-first ideally).
- Are comfortable owning workflows that span infrastructure, product usage, and user-facing behavior.
What You Bring
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Master’s degree or higher in Computer Science, Engineering, Mathematics, or AI-related fields.
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Proficiency in Python and data analysis.
- Prior experience leading LLM projects.
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Exceptional communication skills: ability to convey complex technical concepts clearly.
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Strong project management and organizational skills.
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Passion for AI and the intersection of technology, product, and customer needs.
Minimum Qualifications
- Strong experience with Python and backend APIs.
- Experience with production-grade data pipelines or workflow engines.
- Experience with robotics datasets (video, depth, LiDAR, telemetry, pose).
- Experience with evaluation, scoring, or reliability systems.
- Experience with cloud environments (GCP/AWS preferred).
Bonus Qualifications
- Prior work with robotic perception or manipulation datasets.
- Knowledge of dataset versioning and lineage tracking.
- Experience with cost optimization around video lifecycle or automated review systems.
- Experience designing contributor ramp-down or trust-score systems.
Why This Role Matters
This role has direct financial and model-quality impact. Scaling robotics programs without structured workflows results in exponentially increasing QA spend, storage cost, and rework cycles. Your systems allow robotics customers to confidently scale data throughput from tens → thousands → tens of thousands of labeled sequences without compromising reliability.
You are the backbone of robotics execution at Labelbox.
Alignerr Services at Labelbox
As part of the Alignerr Services team, you'll lead implementation of customer projects and manage our elite network of AI experts who deliver high-quality human feedback crucial for AI advancement. Your team will oversee 250,000+ monthly hours of specialized work across RLHF, complex reasoning, and multimodal AI projects, resulting in quality improvements for Frontier AI Labs. You'll leverage our AI-powered talent acquisition system and exclusive access to 16M+ specialized professionals to rapidly build and deploy expert teams that help customers, which include the majority of leading AI labs and AI disruptors, achieve breakthrough AI capabilities through precisely aligned human data—directly contributing to the critical human element in advancing artificial intelligence.
