Founding Applied AI Engineer

Employment Type: Full-Time Location: Remote / Miami preferred

About Us

Atomic is the venture studio that co-founds companies by pairing founders with the best ideas, teams, and resources, and funding those with the most potential. When entrepreneurs co-found with Atomic, they team up with an experienced group of operators who have started dozens of companies and created billions of dollars in enterprise value.

Industry disruptors like Bungalow, Found, Hims and Hers, Homebound, OpenStore, and Replicant all started at Atomic along with dozens more. Atomic was founded in 2012 by serial entrepreneur Jack Abraham and has offices in NYC, Miami, and San Francisco with a distributed team across North America.

Position Overview

We’re a stealth-mode, AI-native startup reimagining how ecommerce brands connect with their customers across the entire commerce lifecycle, starting with revenue recovery. Our first product is a white-glove recovery tool that transforms customer drop-off into conversions, unlocking precision, personalization, and rich zero-party data that helps shape everything from marketing to product development.

We're backed by top-tier investors, operating with significant seed funding, and hiring our first wave of builders. We're now looking for an Applied AI Engineer to join our founding team and help architect and scale our agentic platform from 0-1. You’ll work at the heart of our AI stack, rapidly prototyping, evaluating, and deploying systems that sit at the heart of the customer experience.

If you thrive in high-autonomy, high-ambiguity environments and are excited to work at the cross section of research, infrastructure, and product to bring bleeding-edge AI to life, we’d love to meet with you!

The Role

As our Founding Applied AI Engineer, you’ll partner directly with the founders and core team to build, test, and ship LLM-driven agent workflows that power our customer experience. This is a hybrid role reporting to the CTO, where you’ll act as both an AI tinkerer and product builder–rapidly testing ideas, evaluating LLM behavior, and turning prototypes into production-ready systems.

What You’ll Do

• Lead end-to-end development of LLM-powered features, from prompts and agents to sub-graphs, rerankers, and eval pipelines.
• Architect and scale infrastructure for experimentation, fine-tuning, and rapid iteration across agent workflows.
• Make strong decisions around context engineering, determining what information to include or exclude for optimal LLM performance.
• Analyze large volumes of AI traces to identify behavioral patterns and use those insights to design and prioritize experiments, both for refining existing features and building net-new functionality.
• Define success metrics for agent performance and customer impact, aligning closely with business objectives.
• Design and execute quantitative and qualitative eval frameworks that measure agent performance, especially where quality is hard to define numerically.
• Partner directly with the CTO to shape technical strategy, make build-vs-buy decisions, and prioritize foundational AI investments.
• Collaborate closely with product and engineering teams to scope, spec, and optimize tool interfaces and endpoints for LLM use across the broader system.
• Work with human experts to translate domain-specific knowledge and workflows into LLM-compatible experiences, extracting tacit knowledge and making it AI-leverageable.
• Stay on the frontier of LLM development, evaluating new research and running POCs to test promising techniques.

Who You Are

• You know your way around the applied AI stack–prompting, embeddings, vector DBs, fine-tuning, and aren’t afraid to get your hands dirty.
• You’ve built and shipped real LLM-powered features, whether it's agents, graphs, or custom assistants that live in production and understand the difference between a novel demo and a performant, reliable product.
• You have experience making principled decisions about data context and grounding, what information gets passed.