Research Engineer - Multimodal AI
Listed on 2026-07-26
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IT/Tech
AI Engineer (Applied/Software), Machine Learning/ ML Engineer, Data Scientist, AI Business & Operations
About Orbifold AI Orbifold AI is redefining how the world builds and scales multimodal AI by pioneering intelligent data curation and workflow engines. In a world overwhelmed by noisy, fragmented data, we help Fortune 500 enterprises and next-generation AI innovators transform raw multimodal data into high-quality, business-aligned training and evaluation pipelines, purposefully built for modern AI systems.
About Orbifold AI Orbifold AI is redefining how the world builds and scales multimodal AI by pioneering intelligent data curation and workflow engines. In a world overwhelmed by noisy, fragmented data, we help Fortune 500 enterprises and next-generation AI innovators transform raw multimodal data into high-quality, business-aligned training and evaluation pipelines, purposefully built for modern AI systems. Backed by Bonfire Ventures, Fusion Fund, and other top investors, our team has led large-scale data curation efforts and contributed to foundational models including Gemini, LLaMA, and Qwen.
Now, Orbifold is creating the enterprise standard for AI-native data infrastructure, powering real-world AI deployment at scale.
About The Role As a Research Engineer at Orbifold AI, you will be at the forefront of building advanced AI models and a data distillation platform that powers our multimodal AI infrastructure. Your primary focus will be developing complex visual models and optimizing AI-driven data platforms, transforming vast, unstructured datasets into high-quality inputs for training, RAG, and reinforcement learning. We are looking for engineers who are passionate about multimodal AI, excel in optimizing complex systems, and have a strong background in computer vision.
Your contributions will drive breakthroughs in multimodal AI, expanding the capabilities of visual models and unlocking new applications across industries.
- Develop, implement, and maintain MoE models, data pipelines, batch inference operating at internet scale, processing large, multimodal datasets including images, text, and videos.
- Integrate the latest research methods into our multimodal models and data distillation platform, ensuring high standards of data accuracy, relevance, and diversity.
- Innovate and experiment with new SOTA models and data curation techniques to maximize the quality and efficiency of training advanced multimodal models.
- Continuously improve data & AI infrastructure to support scalability and flexibility as models and data requirements evolve.
- Bachelor’s or advanced degree in Computer Science or a related field.
- Proficiency in Python and experience with large open source datasets like Data Comp.
- Solid understanding of distributed computing and experience working with large-scale, high-throughput systems.
- Hands-on experience with visual data and multimodal model training.
- Familiarity with deep learning frameworks, especially for handling multimodal data in model training.
- Strong interest in large-scale visual model research and comfort working in a rapidly evolving, dynamic environment.
- Work on groundbreaking AI research in multimodal model training and enterprise AI.
- Gain hands-on experience with large-scale AI datasets and AI-native applications.
- Collaborate with AI leaders from Google, Meta, and Alibaba to shape the future of enterprise AI.
- Opportunity to contribute to real-world AI innovations with Fortune 500 impact.
- Flexible work culture in a fast-moving AI startup.
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