Diffusion Models Machine Learning Expert

Job Description • 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. 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 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 Company Description • AfterQuery is a research lab investigating the boundaries of artificial intelligence through novel datasets and experimentation. We're backed by top investors, including Y Combinator and Box Group, and support all leading AI labs.