LATAM Operations Lead
XDOF
LATAM Operations Lead
XDOF
Data pipelines and annotation for robotics
Role Mission
Own the build-out of XDOF’s ground-level data acquisition operation in LATAM (Mexico/Brazil), taking the region to a fully operating, self-auditing local network. This role owns the end-to-end pipeline of securing high-fidelity physical-world data (Egocentric, Teleoperation, etc.) — from signing individual high-value venues to institutionalizing multi-scenario local partnerships.
Location:
Based in Mexico or Brazil.
Responsibilities
Data collection scene expansion: Own the ground-level expansion strategy and 0-1 execution to build an expanding pipeline of robotics data collection - developing and closing a diversified network of data collection venues and access points (retail, logistics, industrial, urban, etc.)
Team Building & Management: Recruit, train, and manage data collection execution teams. Establish SOPs to ensure data integrity and operational velocity.
Efficiency Optimization: Monitor offline operational data to optimize workflows and staffing, controlling cost per usable hour while maximizing output.
Hardware management: manage the deployment, maintenance and calibration of multi-modal data collection hardware.
Operational Optimization and Feedback: Monitor offline operational data to optimize workflows and staffing, provide clear operational feedback to central teams; document market learnings as SOPs/playbooks for reuse in future markets.
Qualifications
3–5+ years of experience managing field operations, growth, or ground execution for major tech platforms, IoT companies, or hardware-enabled startups.
Proven track record of sourcing, onboarding, and managing SME partnerships and vendor networks (Required).
Working proficiency or fluency in Spanish, Portuguese, or other target-market languages is highly preferred.
Solid grasp of Embodied AI and machine learning data fundamentals (e.g., Teleoperation workflows, Egocentric/First-Person vision requirements) with the ability to translate technical data specifications into field operational blueprints.
A resilient, "builder" mindset with a high tolerance for ambiguity, comfortable traveling frequently and executing directly in unstructured or high-friction environments.
Strong cross-functional capability, matching analytical funnel management (SQL/Excel, conversion rates) with high-stakes, face-to-face field negotiation.
XDOF builds data pipelines, data-collection tools, and annotation systems that frontier AI labs and robotics companies use to train robots to operate in the physical world. It tackles the data bottleneck in physical AI by combining teleoperation, simulation, and egocentric data collection, then layering in data cleaning, tooling, and annotation to prepare usable datasets. The company differentiates itself through its end-to-end data infrastructure for physical AI, its mix of teleoperation, simulation, and first-person data methods, and its Berkeley-founded background from 2024. Its goal is to enable robots to learn to operate in the real world by providing the pipelines and labels needed for training.
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