AI Research Engineer (Multi-Modal & Vision)
100% Remote WorldwideFull-TimeMiddle
Salary not disclosed
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Job Details
- Languages
- English
- Required Skills
- GitHubComputer Vision
Requirements
- 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.)
Responsibilities
- 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.
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