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QU

Operations Lead – AI Data Operations

Quantigoai
Posted 2 hours ago
🌍Worldwide🏠Remote📁Operations & Admin
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Who We Are At Quantigo AI, we combine human expertise with machine efficiency to deliver high-quality annotated data at scale. We help AI companies build reliable datasets faster while maintaining precision and quality. Our clients range from ambitious startups and research labs to Fortune 500 companies across industries including robotics, autonomous systems, computer vision, geospatial intelligence, and multimodal AI applications. As demand for increasingly complex training data grows, we are building scalable operations capable of supporting sophisticated data pipelines, multimodal datasets, and globally distributed contributor teams. This role will play an important part in shaping how our operations evolve over the next phase of growth. What We're Looking For We are seeking an Operations Lead who will take ownership of our data operations and help build the operational foundation required to support complex AI data programs. In this role, you will oversee the execution and delivery of annotation programs while designing the workflows, quality systems, and operational frameworks needed to scale effectively. You will work closely with delivery teams, project managers, QA teams, and leadership to ensure projects are executed efficiently and consistently. Acting at the center of our delivery infrastructure, you will translate client requirements into operational workflows, coordinate global contributor teams, and continuously improve the systems that support project delivery. This role involves working with evolving requirements, managing multiple concurrent projects, and bringing structure to new and complex problem spaces. This is a role for someone who wants to go beyond managing operations within an existing system. You will help define how those systems are built and improved over time. If you are motivated by ownership, problem-solving, and building structure in fast-moving environments, this role will be a strong fit. Key Responsibilities Own the execution and delivery of AI data labeling programs across multiple clients and datasets, ensuring timelines, quality standards, and client requirements are consistently met Build, refine, and maintain operational systems, workflows, and playbooks that support scalable and repeatable project delivery Manage the full lifecycle of annotation programs from onboarding through delivery, including pilots, experimental workflows, and production-scale datasets Translate client requirements into structured workflows, guidelines, and processes for internal teams and contributor networks Coordinate across project managers, QA teams, and distributed contributors to maintain alignment and ensure smooth execution Design and improve annotation workflows and quality assurance frameworks for complex and multimodal datasets Identify operational bottlenecks, capacity constraints, and delivery risks, and implement improvements to increase efficiency and accuracy Track and monitor key operational metrics, including throughput, quality performance, delivery timelines, and overall efficiency Maintain visibility into project health and use performance insights to continuously improve workflows and delivery systems Support the onboarding of new client programs and ensure alignment between client expectations and operational execution Establish best practices for managing globally distributed teams and contribute to building scalable operational structures Who You Are 6–10 years of experience in operations, program management, or delivery roles, with direct experience in AI data services, data labeling, or data annotation companies Experience managing large-scale annotation programs involving distributed teams, contributor networks, and structured quality frameworks, while handling multiple concurrent projects, pilots, or experimental datasets Familiarity with multimodal datasets such as image, video, spatial, sensor, or language data, and experience designing or improving annotation workflows and operational processes Strong understanding of how labeled datasets are used in machine learning pipelines and how data quality impacts model performance Experience using operational and project management tools such as ClickUp, Jira, Notion, Airtable, or similar platforms Comfortable working with globally distributed teams and managing delivery environments across time zones or shift-based operations Resourceful and adaptable, with the ability to operate in fast-moving environments, bring structure to ambiguity, and drive outcomes with limited oversight Why Join Us At Quantigo AI, you will work on problems that are central to how modern AI systems are built. As datasets become more complex, the way they are produced and managed is evolving quickly. This is an early-stage role where you will have direct ownership of how operations are structured and scaled. You will work closely with leadership, contribute to building core systems, and see a clear impact from your work. You can expect: Ownership across projects and operational systems Exposure to complex, real-world AI data challenges The opportunity to improve how data operations are executed at scale If you prefer highly structured environments with established processes, this role may not be the right fit. If you are motivated by building systems, solving operational problems, and taking responsibility for outcomes, this role will suit you. Position Details Industry: Machine Learning, Data Annotation Position Type: Full-time (Permanent) Location: Remote (Global) Start Date: Immediately Salary: Compensation will be benchmarked against the candidate's location, experience, and market rates Other Benefits: As per the organization's policy Equal Opportunity Employer Quantigo AI is an equal opportunity employer. We do not discriminate based on race, religion, gender, sexual orientation, physical or mental disability, age, or any other characteristic. We believe diversity and inclusion make us stronger and we celebrate those differences for the benefit of our employees, services, and community.

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