Multimodal Generative AI Researcher

Benefits

Parental leave

Not verified

Non-birth-parent leave

Not verified

Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

Not verified

Relocation assistance

Not verified

Childcare support

Not verified

Learning budget

Not verified

Verification

Not verified

Salary

Not verified

Was this benefit information wrong? Tell us.

Role

Seniority

Mid From the posting source

Work mode

Remote From the posting source

In-office days

0 days From the posting source

Schedule

Shift type

Not verified

Weekend work

Not verified

Application

Cover letter

Not verified

Assessment

Not verified

Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Multimodal Generative AI Researcher Remote 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

Read the full description at stability.ai. FewerJobs shows a preview and links to the original posting.