Research Engineer, Synthetic Data Generation
Listed on Meta Platforms’s official careers site. Listed as of October 18, 2026. Check that it is still open and apply there.
Apply on Meta Platforms’s careers site →Job description
Meta Reality Labs is looking for a lead computer vision engineer to drive synthetic data generation and simulation for our AR/VR products, including the Quest line. This role will contribute to the creation of next-generation AR/VR interaction technologies in a cross-functional team of software, infra, computer vision, machine learning, simulation, and AI image generation engineers. This role will leverage expertise in computer vision, synthetic data, and AI-based image generation to contribute to ML models shipped in Meta AR/VR products.
This role collaborates with cross-functional teams on image simulation, core AI, computer graphics, machine vision research, and HW design. The immediate team for this role is composed of Computer Vision, Machine Learning and Software Engineers. We work cross-functionally with teams across multiple disciplines, such as simulation, AR/VR optics, Reality Labs Research, silicon, and sensors, to design and develop novel ML solutions tailored for the requirements and constraints of each particular device.
Responsibilities
- Explore, innovate, and leverage novel technologies, from internal and external publications, to improve the realism, diversity, controllability, and generalizability of image synthesis, including techniques to enhance appearance and geometric realism, conditional image generation based on specific conditions or constraints, such as metadata, text-based categories, image masks, or multiple input modalities, and controllable image generation with manipulation of specific attributes like style and appearance Hands-on contribution to synthetic image generation using deep generative models, computer graphics, or physics-based simulation Tech lead for development and launch of CV/ML solutions for generating and synthesizing images used in the training of AR/VR models and system design
- Drive cross-functional collaborations with partner teams, including but not limited to, Reality Labs
- Research, simulation, data systems, and Meta Superintelligence Lab Co-own strategic planning, OKR setting, and roadmap generation for synthetic data and other R&D initiatives
- Contribute to recruitment, mentoring, onboarding, and supporting the professional development of ML engineers/scientists, interns, and contractors
- Optimize machine learning models for performance, scalability, and maintainability
- Collaborate with other engineers and data scientists to develop and deploy machine learning solutions
Qualifications
- Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience PhD in Computer Science or a related field with published projects in the fields of synthetic data generation, machine learning, deep learning, and/or computer vision
- 5+ years of technical leadership experience driving medium to large-scale cross-functional R&D projects focused on machine learning and computer vision Experience implementing computer vision algorithms and deep learning models in C++ and Python, including hands-on work with 3D geometry, image processing, or sensor fusion Experience in communicating complex technical research to cross-functional audiences through written documents, design reviews, or conference presentations Problem-solving and interpersonal skills and experience to lead technical projects independently Experience in classical image processing and pose estimation Experience in computer graphics, procedural generation, and avatar rendering
- Demonstrated track record of peer-reviewed publications in computer vision or machine learning at venues such as CVPR, ICCV, ECCV, NeurIPS, ICLR, or equivalents
- Work experience in the AR/VR industry Experience in designing data collection protocols and quality control, manifested in solid contributions in constructing high-quality computer vision and machine learning datasets
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