We are looking for an Applied AI Engineer to help us make Foley truly AI-native — not by layering tools on top, but by rethinking how work gets done across the company.
You'll operate as a forward-deployed builder, embedded with business teams to identify high-leverage problems and ship real solutions. This is a hands-on role where you'll design, build, and deploy AI-powered systems that improve how we operate, make decisions, and deliver value.
This is not a research role. You build, you ship, and you measure impact.
Build & Ship Solutions
Design and build AI-powered systems end-to-end, including agentic workflows, internal tools, automation pipelines, and business applications
Own your work from concept through production, including iteration and improvement
Use AI-assisted development and modern tooling to move quickly without sacrificing quality
Embed with the Business
Work directly with operations, compliance, sales, and product teams to understand real workflows and pain points
Translate ambiguous, messy processes into clear, scalable systems
Operate as part of cross-functional "tiger teams" focused on high-impact problems
Drive Outcomes, Not Just Output
Define success metrics and measure whether your solutions improve speed, quality, or decision-making
Close the loop by evaluating what worked, what didn't, and what to improve
Build for Scale & Reuse
Design composable solutions that integrate into the broader platform
Think in reusable capabilities, not one-off scripts
Operate with Judgment & Guardrails
Build with awareness of regulatory constraints (DOT, FMCSA, PII)
Apply strong judgment when using AI to ensure quality, trust, and compliance
Contribute to the Builder Community
Share patterns, tools, and learnings with other engineers and teams
Review work and help raise the bar for how we build
We're looking for builders who think in systems and care about outcomes. You may come from different paths, but you share a common trait: you build things that work.
You might come from:
Data Science → GenAI Builder: You moved from analysis to building production systems
Business-Facing Engineer: You bridge technical execution and business context
Domain Expert Turned Builder: You started in a business function and taught yourself to build solutions
Must-haves
You have built and shipped real products, tools, or systems (portfolio, GitHub, or equivalent)
You are fluent in Python, including APIs, data handling, and deployment patterns
You have hands-on experience building with LLMs (e.g., prompting, tool use, RAG, agents)
You think in systems and can map business workflows to technical solutions
You communicate clearly with both technical and non-technical stakeholders
Strong Signals
You've built and deployed multi-step or tool-using AI systems
You've worked with orchestration frameworks (e.g., LangGraph, CrewAI, MCP)
You've built solutions involving document processing, structured extraction, or voice
You've implemented observability for AI systems (e.g., Langfuse, LangSmith)
You understand knowledge graphs or entity resolution
You've worked in regulated environments (transportation, finance, healthcare, compliance)
You've independently taken a process from "this is broken" to a shipped solution
Not Required
A specific degree or traditional background
Experience at large tech companies
ML research or publications
Deep infrastructure or DevOps specialization
Location: This role is primarily remote (US based), with the expectation of occasional visits to our offices for team collaboration, training, or company events.
This role may be based remotely out of the following states: Arizona, California, Colorado, Connecticut, Florida, Georgia, Illinois, Indiana, Kansas, Maryland, Massachusetts, Michigan, Nebraska, New Hampshire, New Jersey, New York, North Carolina, Pennsylvania, South Carolina, Tennessee, Texas, Virginia, Wisconsin.