Diffusion Models Machine Learning Expert

This is a remote, project-based role for machine learning researchers and engineers with deep, specialized expertise in diffusion models. You will complete tasks at the frontier of diffusion-based generative AI — including model development, architectural experimentation, fine-tuning, and research tasks spanning image, video, audio, and scientific data generation. 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.

Commitment: 10 hours/week | Pay: $150 - $200/hr | Type: Contract

Responsibilities

• Design, implement, and evaluate diffusion model architectures including DDPM, score-based, flow matching, and latent diffusion approaches
• Develop and experiment with novel sampling strategies, noise schedules, and guidance techniques to improve generation quality and efficiency
• Fine-tune and adapt pre-trained diffusion models for specific domains, modalities, and downstream tasks
• Conduct rigorous benchmarking of diffusion models across perceptual quality, diversity, and controllability metrics
• Document methodologies, experimental results, and technical approaches clearly and reproducibly

Required Qualifications

• Published researcher with at least one first-author publication in a peer-reviewed venue (e.g., NeurIPS, ICML, ICLR, CVPR, or equivalent)
• Master's or PhD in Machine Learning, Computer Science, Statistics, or a related quantitative field
• Deep, demonstrated expertise in diffusion models, score-based generative models, or flow-based approaches
• Strong problem-solving skills and ability to work independently on open-ended research and engineering tasks

Preferred Qualifications

• Hands-on experience with leading diffusion model frameworks and codebases (e.g., Stable Diffusion, EDM, DiT, Consistency Models, or similar)
• Familiarity with advanced conditioning and control mechanisms (e.g., ControlNet, classifier-free guidance, IP-Adapter, or similar)
• Experience applying diffusion models beyond images — e.g., video, audio, 3D, or scientific data generation
• Background in TA'ing or teaching generative modeling, deep learning, or computer vision 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 diffusion model research and applications
• Professional Growth – Sharpen your skills on varied, challenging generative modeling tasks across modalities