Staff Applied AI Engineer

Arcade•Tracy, CA

2d•Onsite

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. We're looking for a Staff Applied AI Engineer to architect the ML systems at the core of Arcade's product. This is a senior, high-leverage individual-contributor role for someone who thinks in systems — not just models. You'll own the architecture that lets our generative AI scale: the pipelines, the evaluation infrastructure, and the production systems that turn diffusion models and LLMs into reliable, high-quality output at volume. You'll be the person who sees where the leverage is — the system-level changes that move our ML operations and output quality forward by a step function rather than a percentage point — and who brings state-of-the-art, industry best-practice rigor to how we train, deploy, evaluate, and improve models. You'll set the technical direction for applied AI at Arcade and raise the bar for everyone building alongside you.

Requirements

8+ years of engineering experience, with a strong track record of architecting and shipping production ML systems — formal title matters less than the scope and impact of the work.
Demonstrated systems thinking — you've designed the infrastructure (training pipelines, eval systems, serving architecture) that let an entire team scale ML output and quality, not just individual models.
Strong proficiency in Python and comfort in Linux environments; deep hands-on experience with ML libraries such as PyTorch and/or TensorFlow.
Deep fundamentals in machine learning — you understand algorithms "under the hood," not just how to call them.
Proven experience training, fine-tuning, and evaluating models on large-scale datasets, plus solid command of data preprocessing, cleaning, and feature engineering.
Strong working knowledge of recent developments in generative AI, diffusion models, and LLMs — and judgment about which advances are worth adopting.
Experience deploying and maintaining AI models in cloud-based production environments at scale.
Excellent problem-solving and analytical skills with close attention to detail.
Excellent communication and collaboration skills in a fast-paced, cross-functional environment.
Bachelor's or Master's (or equivalent experience) in Computer Science, Mathematics, AI/ML, or a related field.

Nice To Haves

Production experience with text-to-image / diffusion systems specifically.
Experience building ML platform or infrastructure at scale (distributed training, model serving, eval/observability tooling).
A track record of technical leadership — patterns and systems you built that other engineers went on to build on.

Responsibilities

Architect the ML systems behind Arcade's generative platform — design the pipelines, training infrastructure, and serving systems that let diffusion models, LLMs, and emerging generative architectures run reliably and efficiently at production scale.
Find and build the high-leverage systems work — identify the architectural changes that scale our ML operations and meaningfully improve model output and quality, and lead them from design through production.
Set the evaluation and quality bar — design measurement systems (not one-off benchmarks) that real engineering decisions are made against, so model and pipeline quality is defensible with data.
Train, fine-tune, and deploy models — diffusion / text-to-image models and LLM-based applications, including advanced prompt engineering, fine-tuning, retrieval, and multi-component workflows.
Own production reliability — deploy, monitor, and maintain models in cloud-based production environments, ensuring scalability, latency, and cost are engineered intentionally.
Build the data foundation — design systems to collect, clean, and analyze large-scale datasets that improve model performance and reliability over time.
Bring state-of-the-art practice in-house — stay current with developments in generative AI, evaluate what's worth adopting, and translate the best of it into our pipelines.
Set technical direction and raise the bar — establish the patterns and standards the rest of the AI team inherits, and mentor engineers as the team scales.
Communicate clearly across audiences — make technical tradeoffs legible to both technical and non-technical partners across engineering, product, and design.