About this opportunity
Samson Rose lists this Machine Learning Engineer opportunity in northern, Kentucky. Review the employer’s description below for duties, qualifications and application requirements.
Job description
About the Company
Our client is a high-growth, AI-forward consumer platform that has already reached millions of users and is scaling fast. Their product blends thoughtful, expert-driven guidance with intelligent technology, and they're building the next generation of that experience now. The team is small, high-ownership, and moves quickly, and we're looking for people who want to build something new and be part of shaping it from the ground up.
The Role
This is not a research or data science role. They're looking for a strong backend engineer with hands-on experience shipping ML-powered features in production. You'll sit at the intersection of backend systems and machine learning, building the infrastructure and services that bring intelligent, personalized experiences to our users.
You should be comfortable training and deploying models, not just calling them through an API. Your primary identity is as a software engineer who writes clean, production-grade code, with the depth to reason about ML systems and bring them to life in the product. You'll work closely with product, design, mobile, and data partners, and we're looking for someone comfortable moving across the stack rather than staying inside a narrow ML lane.
What You'll Own
Build and improve a RAG-based content recommendation pipeline, with a near-term push toward more agentic, tool-calling behavior
Train and deploy models directly, this is real ML engineering, not wrapping an API around a model someone else built
Design and ship APIs (FastAPI preferred) that expose ML functionality to the rest of the product
Build data pipelines and infrastructure supporting model serving, feature storage, and real-time personalization
Partner directly and continuously with non-technical product and content teams, proposing solutions rather than just executing a spec
Stay current on modern LLM techniques and tooling (agentic patterns, tool calling, coding-assistant workflows) and bring that fluency into everyday engineering work
Own the reliability, performance, and scalability of ML-adjacent backend systems
What You'll Need
3+ years of professional ML engineering experience. Senior candidates in the 5 to 10 year range are also a strong fit
Real, hands-on experience training and deploying models, not just integrating pretrained models via API
Strong software engineering fundamentals: someone who has shipped end-to-end product work, not spent years narrowly specialized on research or model training
Python required, with experience in a framework like FastAPI, Django, or Flask
Experience with cloud infrastructure (AWS preferred) and containerized deployments
Familiarity with vector databases and the broader data stores and pipelines that support ML workloads
Current, hands-on fluency with modern LLM tooling and agentic development patterns
Excellent communication skills, specifically the ability to translate technical tradeoffs for non-technical stakeholders. This is a genuine collaboration role, not order-taking
Computer science or math degree, or equivalent practical experience
Must be authorized to work in the US without sponsorship, or a UK citizen if based in the UK
Nice to Have
Startup experience, ideally on a small team moving at a fast pace
Personal projects or independent build history alongside professional work
Experience with LangChain, LangGraph, or similar orchestration frameworks
Background in recommendation systems, personalization, or content ranking
Experience at a modern, AI-forward consumer or B2B company (health tech and fintech backgrounds translate well)
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Who can apply
Review the original listing for work authorization, qualifications and employer requirements.