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 high-quality multimodal datasets for domain-specific tasks.
Adapt models for resource-constrained environments using compression and optimization techniques.
Design evaluation frameworks and benchmarks for model performance, robustness, and real-world task success.
Build and scale training workflows across distributed GPU infrastructure and resolve training bottlenecks.
Contribute to and leverage open-source models, datasets, and tooling.
Track multimodal learning research and translate 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; 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 results on benchmarks or real-world tasks.
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.
Excellent English communication skills.

Remote work from locations around the world.
Opportunity to work with a small, high-caliber team on multimodal AI and digital-finance technology.
Direct impact on AI model development and deployment at scale.

201-500 employees