Forward Deployed Engineer - Robotics

At Labelbox, we're building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018, we've been pioneering data‑centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially.

We're the only company offering three integrated solutions for frontier AI development:

• Enterprise Platform & Tools
  :
  Advanced annotation tools, workflow automation, and quality control systems that enable teams to produce high‑quality training data at scale
• Frontier Data Labeling Service
  :
  Specialized data labeling through Alignerr, leveraging subject matter experts for next‑generation AI models
• Expert Marketplace
  :
  Connecting AI teams with highly skilled annotators and domain experts for flexible scaling

• High‑Impact Environment
  :
  We operate like an early‑stage startup, focusing on impact over process. You'll take on expanded responsibilities quickly, with career growth directly tied to your contributions.
• Technical Excellence
  :
  Work at the cutting edge of AI development, collaborating with industry leaders and shaping the future of artificial intelligence.
• Innovation at Speed
  :
  We celebrate those who take ownership, move fast, and deliver impact. Our environment rewards high agency and rapid execution.
• Continuous Growth
  :
  Every role requires continuous learning and evolution. You'll be surrounded by curious minds solving complex problems at the frontier of AI.
• Clear Ownership
  :
  You'll know exactly what you're responsible for and have the autonomy to execute. We empower people to drive results through clear ownership and metrics.

Get an inside look and hear directly from our current Forward Deployed Engineers
here !

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.

• Build and optimize ingestion pipelines for robotics video, synchronized metadata, sensor logs, and derived annotations.
• Architect scalable labeling workflows that maintain , 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.
• 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.
• 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.

Master’s degree or higher in Computer Science, Engineering, Mathematics, or AI‑related fields.

Proficiency in Python and data analysis.

• Prior experience leading LLM projects.

Exceptional communication skills: ability to convey complex technical concepts clearly.

Strong project management and organizational skills.

Passion for AI and the intersection of technology, product, and customer needs.

• 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.
• Experien…