Applied AI Researcher

About the Role

We are seeking a Senior AI/ML Research Scientist to advance the next generation of Generative AI capabilities across creative content generation and foundation models. This role will focus on developing, adapting, and optimizing state-of-the-art AI models, including diffusion models, LLMs, multimodal architectures, and Graph Neural Networks (GNNs).

Responsibilities

• Lead research and development of state-of-the-art generative AI systems, including diffusion models, latent diffusion architectures, and multimodal foundation models.
• Collaborate with stakeholders to understand business needs and translate them into model requirements. Advise on what is possible and the impact that the models can drive.
• Design, train, fine-tune, and optimize large-scale AI models for creative content generation.
• Develop and apply parameter-efficient adaptation techniques, including LoRA, adapters, prompt tuning, and related methods for foundation model customization.
• Advance internal LLM capabilities through model training, fine-tuning, evaluation, alignment, and optimization.
• Research and implement Graph Neural Network (GNN) architectures to model complex relationships, knowledge graphs, recommendation systems, and structured data representations.
• Evaluate emerging research and rapidly prototype innovative approaches from leading conferences and publications.
• Collaborate with engineering teams to transition research innovations into scalable production systems.

Qualifications

• Minimum Qualifications:
• PhD or Master's in Computer Science, Machine Learning, Artificial Intelligence, or a related quantitative field.
• 7+ years of experience conducting advanced machine learning research and development with a track record of translating cutting-edge research into impactful products or platforms.
• Strong publication record in leading AI conferences or journals (NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, EMNLP, KDD, AAAI, or equivalent).
• Deep expertise in deep learning fundamentals, optimization, representation learning, and neural network architectures.
• Strong programming skills in Python and experience with modern ML frameworks such as PyTorch, JAX, or TensorFlow.
• Experience in training and evaluating large-scale machine learning models on distributed compute infrastructure and GPU-accelerated computing environments.
• Preferred Qualifications:
• Expertise in developing and training diffusion models, latent diffusion models, or related generative architectures.
• Experience fine-tuning foundation models using LoRA, QLoRA, adapters, PEFT techniques, RLHF, DPO, or similar approaches.
• Experience developing generative AI systems for image, video, audio, 3D, or other creative content domains.