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 Py Torch / Deep Speed / 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.