AI Research Engineer

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.