Machine Learning Engineer - Computer Vision

The Role: We're looking for an experienced Machine Learning Engineer with a deep background in computer vision and generative AI. You’ll be part of a team developing systems that generate realistic, diverse, and highly controllable synthetic image data using state-of-the-art tools like Stable Diffusion, LoRA, and ControlNet. Your work will directly impact the performance and robustness of real-world AI systems—from self-driving cars to defense robotics.

What You’ll Do

• Develop and fine-tune generative AI pipelines (e.g., Stable Diffusion, ControlNet, LoRA) for synthetic image generation.
• Design and implement machine learning workflows to generate structured, photorealistic datasets.
• Collaborate with 3D artists and simulation engineers to align ML models with physical environments and sensor setups.
• Work closely with customers in automotive, robotics, and aerospace to deliver tailored datasets.
• Optimize generation systems for scalability, quality, and speed using tools like ComfyUI or custom pipelines.

You Should Have

• MSc or PhD in Computer Vision, Machine Learning, or related field.
• 3+ years of hands-on experience with deep learning frameworks (e.g., PyTorch, TensorFlow).
• Experience working with generative AI models (especially diffusion-based models).
• Familiarity with tools like LoRA, ControlNet, ComfyUI, or custom workflows around Stable Diffusion.
• Strong understanding of synthetic data generation and its role in training perception systems.
• A track record of deploying ML systems in domains like automotive, robotics, defense, or manufacturing.

Nice to Have

• Experience working with 3D simulation pipelines or sensor modeling (e.g., depth, LiDAR, segmentation).
• Contributions to open-source ML/gen AI tools or custom diffusion model training.
• Enjoying reading research papers
• A problem solving intellect and a team player spirit
• Knowledge of synthetic-to-real domain adaptation techniques.
• Experience in startup environments or working directly with enterprise R&D teams.
• A positive can-do attitude