Conduct end-to-end research and engineering on vision-language models, covering training, evaluation, and optimization across the full model development lifecycle.
Design and implement post-training pipelines including supervised fine-tuning, knowledge distillation, and reinforcement learning from human feedback.
Develop and maintain high-quality multimodal datasets, including data curation, filtering, and balancing for domain-specific tasks.
Drive model efficiency and deployability, adapting models for resource-constrained environments using compression and optimization techniques.
Design and implement evaluation frameworks and benchmarks to measure model performance, robustness, and real-world task success.
Build and scale training workflows across distributed GPU infrastructure.
Identify and resolve bottlenecks in training pipelines to achieve state-of-the-art model quality on target benchmarks.
Contribute to and leverage open-source ecosystems including models, datasets, and tooling to accelerate development.
Stay current with the latest research in multimodal learning and vision-language systems, translating relevant findings into practical improvements.
Publish research findings in top-tier AI conferences and journals where applicable.

Degree in Computer Science, Machine Learning, or a related field; MS/PhD preferred.
Strong experience with multimodal post-training workflows including supervised fine-tuning, knowledge distillation, and reinforcement learning from feedback.
Hands-on experience with parameter-efficient fine-tuning and distributed training frameworks.
Demonstrated ability to build and improve vision-language models with measurable results on standard benchmarks or real-world tasks.
Experience adapting models for resource-constrained environments.
Proven open-source contributions in multimodal AI on GitHub or HuggingFace.
Publications at top AI conferences (NeurIPS, ICML, ICLR, CVPR, ECCV etc.)

AI Research Engineer – Multi-Modal, Vision

Key skills