Job
Description
Key Responsibilities: Research & Innovation: Conduct applied research in generative AI, foundation models, NLP, computer vision, and multimodal AI. Stay abreast of the latest publications and open-source advancements. Model Development: Fine-tune, evaluate, and optimize large language models (LLMs), transformers, and other generative models for specific business and product use cases. Prototyping & Experimentation: Build proof-of-concepts and experimental systems that demonstrate the potential of GenAI across domains such as content generation, summarization, synthetic data, agent systems, etc. Data & Evaluation Pipelines: Design robust data pipelines, evaluation metrics, and benchmarking systems to validate model performance, safety, and bias. Collaboration: Work with cross-functional teams including product managers, ML engineers, and data scientists to translate research into production-grade systems. Open Source & IP Contribution: Publish findings in peer-reviewed venues, contribute to open-source projects, or generate intellectual property relevant to the business. Required Qualifications: 57 years of experience in machine learning or applied AI roles, with at least 23 years working on generative models or related research. Strong foundation in deep learning frameworks such as PyTorch, TensorFlow, or JAX. Experience with LLMs (e.g., GPT, LLaMA, Claude), diffusion models, or vision-language models. Proficient in Python and ML tools/libraries such as Hugging Face Transformers, LangChain, or similar. Understanding of responsible AI practices, bias mitigation, and model explainability. Master's or PhD in Computer Science, Machine Learning, Mathematics, or related fields. Preferred Qualifications: Experience with open-source LLMs or fine-tuning techniques like LoRA, PEFT, RLHF, etc. Knowledge of MLOps practices and deployment of models in production (e.g., via Kubernetes, Ray, Triton).
