SVP of Data Operations

The SVP of Data Operations owns the end-to-end engine that produces training data for CNTXT AI's AI and robotics models.

CNTXT AI turns enterprise and government data into accurate, compliant, production-ready AI. Its work spans Data Services (cleaning, structuring, and labeling training data), Custom AI Solutions, and an AI Product Lab. Beyond annotation, CNTXT AI collects data for physical AI and robotics, including egocentric (first-person) video and sensor capture and teleoperation demonstration data for robot learning. Lab products include Munsit, an Arabic speech-to-text model covering 25+ dialects, and TestAI, a model validation platform.

The SVP of Data Operations owns the end-to-end engine that produces training data for CNTXT AI's AI and robotics models. This covers data annotation and labeling, real-world robotics data collection, egocentric (first-person video and sensor) capture, and teleoperation programs that generate demonstration data.

You will scale these operations from pilot to industrial volume while raising quality and lowering cost per usable hour or label. You will lead in-house teams and vendor networks from our Milpitas headquarters and partner closely with research, robotics engineering, and ML infrastructure.

• Define the multi-year data operations strategy, tied to model roadmaps and data needs from research.
• Build and lead an organization spanning annotation, field collection, teleoperation, QA, and program management.
• Decide build vs. buy for each data stream, and balance in-house teams with vendor capacity.
• Build CNTXT AI's US data operations hub in Milpitas and run it as one operation with teams in the UAE and MENA, across time zones.

• Run large-scale annotation programs across image, video, 3D/point cloud, LiDAR, multimodal, and language data.
• Own labeling guidelines, taxonomies, ontologies, and annotator training and calibration.
• Manage annotation vendors and BPO partners on throughput, quality, cost, and security.
• Bring in model-assisted and active-learning workflows to cut cost per label.

• Stand up and scale teleoperation programs that capture high-quality demonstration data for robot learning.
• Manage teleoperator recruiting, training, scheduling, ergonomics, and performance, across shifts and sites.
• Operate data collection labs and fleets, including robot uptime, hardware readiness, and task and scene design.
• Work with robotics engineering on teleop rigs, VR/haptic interfaces, and collection tooling.

• Run first-person data capture programs using head-mounted cameras, smart glasses, and wearable sensors.
• Recruit and manage contributor networks for capture in homes, workplaces, and other real environments.
• Ensure task, scene, and demographic diversity in line with research specifications.
• Own informed consent, contributor agreements, and PII handling, including face and voice anonymization.

• Set quality metrics (label accuracy, inter-annotator agreement, episode success rate, usable-hours yield) and hit them.
• Partner with ML infrastructure on ingestion, metadata, curation, and dataset versioning.
• Ensure compliance with CCPA/CPRA, GDPR, BIPA, UAE PDPL, data residency rules, and labor and contractor laws in every region where data is collected.

• Own the budget, unit economics (cost per labeled item, per teleop hour, per usable episode), and capacity planning.
• Report throughput, quality, and cost to the executive team and adjust operations as research priorities shift.
• Build a safe, well-run workplace for operators, collectors, and contributors.

• 9+ years in operations leadership, including 7+ years running data annotation, data collection, or AI training data operations.
• 5+ years at VP level or above, owning budgets of $50M+ and organizations of 200+ including vendor work forces.
• Proven scaling of annotation or data collection programs to millions of labels or thousands of hours per month.
• Direct experience with robotics data collection, teleoperation, or egocentric/wearable data capture.
• Strong command of quality systems, workforce management, and vendor management at scale.
• Working knowledge of ML data needs (dataset design, coverage, and how data quality affects model performance).
• Experience with privacy, consent, and biometric data rules (CCPA/CPRA, GDPR, BIPA).
• Bachelor's…