Machine Learning Engineer – Diffusion and Rectified Flow Image Generative AI

Lit8 develops generative AI systems for real-time image generation and enhancement. In this role, you will develop, train, optimize, and deploy image generative models, with a focus on diffusion, rectified flow, flow matching, and image-to-image generation.

We are looking for someone who has built image generation systems in practice — training, fine-tuning, evaluating, and improving models with a strong focus on image quality, controllability, inference speed, and production performance.

Minimum Qualifications

Lit8 develops generative AI systems for real-time image generation and enhancement. In this role, you will develop, train, optimize, and deploy image generative models, with a focus on diffusion, rectified flow, flow matching, and image-to-image generation.

We are looking for someone who has built image generation systems in practice — training, fine-tuning, evaluating, and improving models with a strong focus on image quality, controllability, inference speed, and production performance.

Minimum Qualifications

• At least 2 years of hands-on experience with diffusion models, rectified flow, flow matching, or closely related image generative models.
• Strong experience with image-to-image generation, including image editing, enhancement, inpainting, denoising, super-resolution, style transfer, or controllable generation.
• Hands-on experience training, fine-tuning, evaluating, and debugging image generation models.
• Strong understanding of diffusion training and sampling, conditioning, guidance, latent-space generation, and image-quality optimization.
• Experience with modern generative architectures such as diffusion models, DiTs, U-Nets, transformer-based image models, or flow-based models.
• Strong Python programming skills and hands-on experience with PyTorch or similar frameworks.
• Understanding of image quality evaluation, including perceptual quality, artifacts, sharpness, realism, consistency, LPIPS, SSIM, FID, or similar metrics.
• Strong problem-solving, analytical, and communication skills.

Preferred Qualifications

• Experience with large-scale model training, distributed training, mixed precision, scalable data pipelines, or dataset curation.
• Experience with controllable generation methods such as ControlNet-style conditioning, adapters, LoRA, guidance mechanisms, or prompt/image conditioning.
• Experience optimizing image generation models for real-time, low-latency, or production inference.
• Experience with model optimization techniques such as distillation, quantization, pruning, graph optimization, operator fusion, or hardware-aware tuning.
• Contributions to open-source ML, computer vision, image generation, or generative AI projects are a plus.

Key Responsibilities

• Develop, train, and optimize diffusion, rectified flow, and flow-matching image generation models.
• Build and improve image-to-image workflows for editing, enhancement, inpainting, denoising, super-resolution, and controllable generation.
• Improve image quality, controllability, latency, memory efficiency, and production readiness.
• Debug and improve training pipelines, datasets, sampling strategies, and evaluation workflows.
• Benchmark models across visual quality, artifacts, latency, memory usage, and stability.
• Collaborate with research, engineering, and product teams to integrate models into production applications.

What We Offer

• The opportunity to work on advanced image generative AI systems with real product impact.
• A fast-moving, research-driven environment focused on technical excellence and ownership.
• Attractive salary.
• If you are passionate about diffusion models, rectified flow, image-to-image generation, and production-grade generative AI systems, we’d love to hear from you.