Multimodal Generative AI Researcher

Multimodal Generative AI Researcher Location: Remote About the Role We’re looking for a Research Scientist with deep expertise in training and fine-tuning large Vision-Language and Language Models (VLMs / LLMs) for downstream multimodal tasks. You’ll help push the next frontier of models that reason across vision, language, and 3D, bridging research breakthroughs with scalable engineering. What You’ll Do Design and fine-tune large-scale VLMs / LLMs — and hybrid architectures — for tasks such as visual reasoning, retrieval, 3D understanding, and embodied interaction. Build robust, efficient training and evaluation pipelines (data curation, distributed training, mixed precision, scalable fine-tuning). Conduct in-depth analysis of model performance: ablations, bias / robustness checks, and generalisation studies. Collaborate across research, engineering, and 3D / graphics teams to bring models from prototype to production. Publish impactful research and help establish best practices for multimodal model adaptation. What You Bring PhD (or equivalent experience) in Machine Learning, Computer Vision, NLP, Robotics, or Computer Graphics. Proven track record in fine-tuning or training large-scale VLMs / LLMs for real-world downstream tasks. Strong engineering mindset — you can design, debug, and scale training systems end-to-end. Deep understanding of multimodal alignment and representation learning (vision–language fusion, CLIP-style pre-training, retrieval-augmented generation). Familiarity with recent trends, including video-language and long-context VLMs, spatio-temporal grounding, agentic multimodal reasoning, and Mixture-of-Experts (MoE) fine-tuning. Awareness of 3D-aware multimodal models — using NeRFs, Gaussian splatting, or differentiable renderers for grounded reasoning and 3D scene understanding. Hands-on experience with PyTorch / DeepSpeed / Ray and distributed or mixed-precision training. Excellent communication skills and a collaborative mindset. Bonus / Preferred Experience integrating 3D and graphics pipelines into training workflows (e.g., mesh or point-cloud encoding, differentiable rendering, 3D VLMs). Research or implementation experience with vision-language-action models, world-model-style architectures, or multimodal agents that perceive and act. Familiarity with efficient adaptation methods — LoRA, adapters, QLoRA, parameter-efficient finetuning, and distillation for edge deployment. Knowledge of video and 4D generation trends, latent diffusion / rectified flow methods, or multimodal retrieval and reasoning pipelines. Background in GPU optimisation, quantisation, or model compression for real-time inference. Open-source or publication track record in top-tier ML / CV / NLP venues. Equal Employment Opportunity: We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or other legally protected statuses.