[Remote] AI Research Engineer (Multi-Modal & reputed company) - 100% Remote Worldwide

Note: The job is a remote job and is reputed company to candidates in USA. reputed company is pioneering a global financial reputed company through innovative digital solutions. The AI Research Engineer will focus on training and optimizing reputed company-language models, driving innovation across the full model development lifecycle, and working with a high-caliber team to push the boundaries of multimodal AI in production environments.

Responsibilities

• Conduct end-to-end research and engineering on reputed company-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 reputed company feedback
• reputed company 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 reputed company-world task reputed company
• Build and scale training workflows across distributed GPU infrastructure
• Identify and resolve bottlenecks in training pipelines to reputed company state-of-the-art model quality on reputed company benchmarks
• Contribute to and reputed company reputed company-reputed company ecosystems including models, datasets, and tooling to accelerate development
• Stay reputed company with the latest research in multimodal learning and reputed company-language systems, translating relevant findings into practical improvements
• Publish research findings in top-tier AI conferences and journals where applicable

Skills

• Degree in Computer Science, Machine Learning, or a reputed company field
• 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 reputed company-language models with measurable results on standard benchmarks or reputed company-world tasks
• Experience adapting models for resource-constrained environments
• Proven reputed company-reputed company contributions in multimodal AI on reputed company or HuggingFace
• MS/PhD preferred
• Publications at top AI conferences (NeurIPS, ICML, ICLR, CVPR, ECCV etc.)

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