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# Machine Learning Engineer

- **Company:** Xcede
- **Location:** United States (Remote available)
- **Salary:** $60,000.0 - $70,000.0
- **Contract:** Permanent contract
- **Skills:** Machine Learning, Software Engineering, Large Language Models, Machine Learning Operations, Data Pipelines
- **Published:** July 19, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/kpcdu6ckzq

## About the Role

* Strong, traditional data science and ML modelling experience - this team's forecasting runs on tree-based models, not LLMs, so this is the technical core of the role
 * Demonstrable experience deploying models into production and owning them once live
 * Comfort with software engineering best practice - building pipelines, ensuring robustness, and following deployment discipline
 * A personable, proactive communicator - this is a small, collaborative team, so it's not a fit for someone who wants to work heads-down and solo
 * Bonus: exposure to LLMs/GenAI, forecasting-specific experience, or a retail background - all genuinely nice-to-haves, not requirements

## Description

We're recruiting on behalf of a retail AI startup that helps some of the UK's best-known consumer goods retailers make smarter pricing and inventory decisions. Their platform combines forecasting, pricing optimisation and automated stock management, all powered by proprietary machine learning models that plug straight into a retailer's planning workflow. They've built a small, tight-knit team, and are now looking to grow their data science function.

What the role involves:

 * Building and improving the forecasting models that power the company's sales forecasting, pricing and buying products
 * Owning models end-to-end - from feature creation and training through to deployment, monitoring and maintenance in production
 * Working across a genuinely broad mix of projects - this is a startup, so no single narrow specialism
 * Building robust, production-grade pipelines using strong software engineering practice, since these models interface directly with retail clients
 * Collaborating closely with a small, cross-functional team, including product, engineering and the founders


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