The job, in the employer's words
About the Role
The Strategic Project Lead – Multimodal AI supports frontier AI development through complex, high-quality data programs for leading AI companies. We’re looking for someone with experience in multimodal AI data, such as image, video, 3D, sensor, or robotics-related workflows.
You’ll lead projects that turn complex real-world data into reliable training and evaluation resources. Combining project management with hands-on domain knowledge, you’ll partner with clients and internal teams to design workflows, maintain quality, and solve challenging data delivery problems in a rapidly evolving AI market.
Your Impact
- Own end-to-end project delivery across scope, timelines, budgets, staffing, and risks.
- Lead client communication, status reporting, planning sessions, and escalations.
- Translate technical and client requirements into data collection workflows, annotation guidelines, tooling, and quality standards.
- Define quality metrics and acceptance criteria for multimodal data and annotation outputs.
- Monitor delivery quality, troubleshoot data issues, and resolve ambiguous requirements with clients.
- Advise on project feasibility, collection methods, annotation approaches, and tooling based on your domain expertise.
- Build reusable SOPs, guidelines, and QA frameworks to support consistent delivery.
- Mentor Operations Specialists and contribute to project scoping, pilots, and emerging multimodal AI capabilities.
What You Bring
- 1–2+ years of experience in AI data operations, project coordination, or delivery, ideally in a GenAI or frontier AI environment.
- Familiarity with at least one multimodal data workflow involving images, video, 3D data, sensors, or robotics.
- Experience coordinating projects or workstreams, managing stakeholders, and communicating with clients.
- Understanding of data quality, annotation workflows, and quality assurance methods.
- Ability to translate project requirements into clear data collection workflows, annotation guidelines, and quality standards.
- Strong problem-solving skills and the ability to manage multiple priorities in a fast-moving environment.
- Interest in current AI developments, including multimodal models, spatial AI, or embodied AI.
Nice to Have
- Experience with image or video annotation, 3D data, point clouds, or sensor data.
- Familiarity with robotics data collection or real-world perception workflows.
- Exposure to 2D or 3D labeling, segmentation, tracking, or structured annotation tasks.
- Experience with autonomous systems, spatial AI, or embodied AI.
- Bilingual capabilities, particularly German or a Nordic language.
Why You'll Love Working Here
At Appen, we foster a culture of innovation, collaboration, and excellence. We value curiosity, accountability, and a commitment to delivering the highest quality AI solutions for frontier models.
You'll work on complex challenges that shape the future of AI across industries and geographies, alongside talented people in a culture that values humility over ego. You'll have the flexibility to deliver in a way that works for you and your team, supported by tools, resources, and development opportunities to continue to build your capability over time.
About Appen
Requirements mentioned
- 1–2+ years of experience in AI data operations, project coordination, or delivery, ideally in a GenAI or frontier AI environment.
- Familiarity with at least one multimodal data workflow involving images, video, 3D data, sensors, or robotics.
- Experience coordinating projects or workstreams, managing stakeholders, and communicating with clients.
- Understanding of data quality, annotation workflows, and quality assurance methods.
- Ability to translate project requirements into clear data collection workflows, annotation guidelines, and quality standards.
- Strong problem-solving skills and the ability to manage multiple priorities in a fast-moving environment.
- Interest in current AI developments, including multimodal models, spatial AI, or embodied AI.
- Experience with image or video annotation, 3D data, point clouds, or sensor data.
- Familiarity with robotics data collection or real-world perception workflows.
- Exposure to 2D or 3D labeling, segmentation, tracking, or structured annotation tasks.
- Experience with autonomous systems, spatial AI, or embodied AI.
- Bilingual capabilities, particularly German or a Nordic language.
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