Applied AI Engineer

Arcade•Presidio, CA

About The Position

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 believe everyone should have the power to create physical goods as easily as they post online, and we’re building the infrastructure to make that real. We’ve raised $42M from a world-class group of investors, including Reid Hoffman, Forerunner Ventures (Kirsten Green), Canaan Partners (Laura Chau), Adverb Ventures (April Underwood), Factorial Funds (Sol Bier), Offline Ventures (Brit Morin), Sound Ventures (Ashton Kutcher), Inspired Capital (Alexa von Tobel), and Torch Capital (Jonathan Keidan). Our angel investors include Elad Gil, Ev Williams, Marissa Mayer, Sara Beykpour, Kayvon Beykpour, Anna Veronika Dorogush, Eugenia Kuyda, David Luan, Sharon Zhou, Kelly Wearstler, Karlie Kloss, Colin Kaepernick, Christy Turlington Burns, and Jeff Wilke. Arcade is headquartered in San Francisco’s Presidio and led by serial entrepreneur Mariam Naficy (Minted, Eve), and a founding team with deep experience in generative AI, design systems, and supply chain. We’re pioneering a new category at the intersection of AI, personal expression, and on-demand manufacturing, and we’re building fast. Overview of Role We are seeking an Applied AI Engineer to join our growing team and help advance our generative AI capabilities. This role blends hands-on model development with integration of cutting-edge AI techniques into production systems. You’ll contribute to research, model experimentation, and implementation working closely with cross-functional teams to bring AI-driven products to life.

Requirements

Bachelor’s or Master’s degree in Computer Science, Mathematics, AI/ML, or a related field
Strong proficiency in Python and familiarity with Linux environments; hands-on experience with machine learning libraries like PyTorch, TensorFlow,, etc.
Strong fundamentals in machine learning concepts, with a deep understanding of algorithms and their underlying mechanics ("under the hood")
Familiarity with training, fine-tuning, and evaluating models, especially with large-scale datasets
Familiarity with data preprocessing, cleaning, and feature engineering
Awareness of recent developments in generative AI, diffusion models, and LLMs
Familiarity with cloud platforms and deploying AI models in cloud-based environments
Excellent problem-solving and analytical thinking skills with close attention to detail
Excellent communication and collaboration skills in a fast-paced and collaborative environment.

Responsibilities

Collaborate with engineering and product teams to train and fine-tune machine learning models, including diffusion models, LLMs, and other emerging generative architectures
Develop tools and pipelines that support scalable, efficient, and production-ready AI systems
Collect, clean, and analyze large-scale datasets to improve model performance and reliability
Deploy and maintain AI models in production environments, ensuring scalability, reliability, and efficiency
Design and deploy text-to-image models and LLM-based applications, including advanced prompt engineering, fine-tuning, and multi-component workflows
Maintain and monitor deployed models in cloud-based production environments
Stay current with developments in generative AI, contribute to the research environment, and apply new developments to enhance pipeline performance
Communicate technical concepts clearly across technical and non-technical audiences