AI Research Engineer (Multi-Modal & Vision)

3 months ago

Remote, WorldwideSenior

Responsibilities

Conduct end-to-end research and engineering on vision-language models across training, evaluation, and optimization.
Design and implement post-training pipelines using supervised fine-tuning, knowledge distillation, and reinforcement learning from human feedback.
Curate, filter, balance, and maintain multimodal datasets for domain-specific tasks.
Adapt and optimize models for resource-constrained environments and real-world deployment at scale.
Build evaluation frameworks and benchmarks for model performance, robustness, and task success.
Build and scale training workflows across distributed GPU infrastructure and resolve pipeline bottlenecks.
Contribute to open-source multimodal AI models, datasets, and tooling.
Translate current multimodal learning research into practical model improvements and publish findings where applicable.

Degree in Computer Science, Machine Learning, or a related field; an MS or PhD is 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 benchmark or real-world results.
Experience adapting models for resource-constrained environments.
Proven open-source contributions in multimodal AI on GitHub or Hugging Face.
Publications at leading AI conferences such as NeurIPS, ICML, ICLR, CVPR, or ECCV.

Remote work with a globally distributed team.
Opportunity to work in a small, high-caliber team on multimodal AI research and production deployment.

201-500 employees