Staff Applied AI Engineer

About the Company

Arcade is building the world's first AI physical product creation platform, where imagination becomes reality. Our platform lets anyone design, purchase, and sell custom, manufacturable products using natural language and generative AI. We've raised $42M from a world-class group of investors to pioneer a new category at the intersection of AI, personal expression, and on-demand manufacturing.

Responsibilities

• Architect the ML systems behind Arcade's generative platform, including pipelines, training infrastructure, and serving systems
• Identify and build high-leverage architectural changes to scale ML operations and improve model quality
• Design measurement and evaluation systems to ensure model and pipeline quality is defensible with data
• Train, fine-tune, and deploy diffusion models, text-to-image models, and LLM-based applications
• Own production reliability by deploying, monitoring, and maintaining models in cloud-based environments
• Design systems to collect, clean, and analyze large-scale datasets to improve model performance
• Set technical direction, establish engineering patterns, and mentor engineers as the team scales

Requirements

• 8+ years of engineering experience with a track record of shipping production ML systems
• Demonstrated experience designing infrastructure such as training pipelines, eval systems, or serving architecture
• Strong proficiency in Python and Linux environments
• Deep hands-on experience with ML libraries like PyTorch or TensorFlow
• Strong fundamentals in machine learning algorithms and large-scale model training/fine-tuning
• Working knowledge of generative AI, diffusion models, and LLMs
• Experience deploying and maintaining AI models in cloud-based production environments at scale
• Bachelor's or Master's in Computer Science, Mathematics, AI/ML, or a related field

Preferred Qualifications

• Production experience with text-to-image or diffusion systems
• Experience building ML platform or infrastructure at scale (distributed training, model serving, observability tooling)
• Proven track record of technical leadership