Image Generation Machine Learning Expert

This is a remote, project-based role for machine learning researchers and engineers with deep expertise in image generation and generative modeling. You will complete tasks at the frontier of generative AI — including model development, fine-tuning, evaluation, and research tasks applied to diffusion models, GANs, autoregressive image models, and related architectures. Work is over the next 2–3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers exceptional pay, exposure to cutting-edge generative AI research, and a strong addition to your research portfolio.

Responsibilities

• Design, build, and evaluate state-of-the-art image generation models including diffusion models, GANs, VAEs, and autoregressive approaches
• Fine-tune and adapt pre-trained generative models for specific domains, styles, and downstream applications
• Conduct rigorous evaluation of image generation quality using perceptual, statistical, and task-specific metrics
• Experiment with conditioning mechanisms, guidance techniques, and controllable generation approaches
• Document methodologies, experimental results, and technical approaches clearly and reproducibly

Requirements

• Published researcher with at least one first-author publication in a peer-reviewed venue (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, or equivalent)
• Master's or PhD in Machine Learning, Computer Vision, Computer Science, or a related quantitative field
• Demonstrated expertise in image generation, generative modeling, or closely related areas of deep learning
• Demonstrated expertise in image generation, generative modeling, or closely related areas of deep learning

Preferred Qualifications

• Hands-on experience with leading generative modeling frameworks and architectures (e.g., Stable Diffusion, DALL-E, Imagen, DiT, or similar)
• Familiarity with fine-tuning techniques such as LoRA, DreamBooth, ControlNet, or similar methods
• Experience with large-scale image datasets and distributed training pipelines
• Background in TA'ing or teaching computer vision, deep learning, or generative modeling courses

Why Apply

• Flexible Time Commitment – Work on your schedule while tackling meaningful research challenges
• Startup Exposure – Work directly with an early-stage Y Combinator-backed company, gaining hands-on experience that sets you apart
• Exceptional Pay – Project-based pay ranges from $150–$200/hour
• Portfolio Building – Gain experience on frontier image generation and generative modeling problems
• Professional Growth – Sharpen your skills on varied, challenging visual generation tasks and datasets