• Contribute to sophisticated research in large language models (LLMs), natural language processing (NLP), and multimodal deep learning • Help build, implement, and evaluate machine learning models and algorithms • Participate in data collection, preprocessing, and analysis • Assist in developing and fine-tuning AI models for various applications • Create experiments, compare algorithms, tune hyperparameters, and document results • Share findings with the team, contribute to reports and presentations, and help onboard new team members

• Translate machine learning research into practical, domain-specific solutions • Own applied research and experimentation across computer vision, LLMs, and AI workflows • Drive the design and execution of experiments end to end • Help shape technical direction by identifying high-impact opportunities • Make pragmatic trade-off calls across model accuracy, interpretability, latency, and cost • Share experience with research teammates to encourage rigor in a collaborative environment

• 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.

• 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.