Machine Learning Customer Engineer

Bloomreach is building the world’s premier agentic platform for personalization .We’re revolutionizing how businesses connect with their customers, building and deploying AI agents to personalize the entire customer journey.

• We're taking autonomous search mainstream, making product discovery more intuitive and conversational for customers, and more profitable for businesses.
• We’re making conversational shopping a reality, connecting every shopper with tailored guidance and product expertise — available on demand, at every touchpoint in their journey.
• We're designing the future of autonomous marketing , taking the work out of workflows, and reclaiming the creative, strategic, and customer-first work marketers were always meant to do.

And we're building all of that on the intelligence of a single AI engine — Loomi AI — so that personalization isn't only autonomous…it's also consistent.From retail to financial services, hospitality to gaming, businesses use Bloomreach to drive higher growth and lasting loyalty. We power personalization for more than 1,400 global brands, including American Eagle, Sonepar, and Pandora.

Purpose of the Role

Enterprise Discovery customers increasingly require hands-on customisation beyond standard product configuration. The Machine Learning Customer Engineer is a dedicated, customer-embedded engineering role designed to own the full spectrum of technical customisation for our top-tier accounts.

You will bridge the gap between post-sales support and core product engineering, serving as a technical "special forces" operative who translates complex business requirements into tangible code and system optimisations.

The Customisation Spectrum

The FDE is purpose-built to own Levels 1–5 of the customisation spectrum, ensuring that core R&D remains focused on next-generation platform development:

• Education : Platform training, feature evangelism, and root-cause explanation.
• Configuration: Dashboard, integration, and algorithm configuration changes.
• Surgical Tweaks: Customer-specific process changes and deep data analysis.
• Custom Code: Conversational agents, platform extensions, and feature customization.
• Escalation: Identifying major product changes for Core Engineering cycles.

Key Responsibilities

• Account Ownership: Maintain ongoing technical ownership of 2–3 high-value enterprise accounts; this is not a rotational assignment.
• Technical Implementation: Build and deploy conversational agents, custom extensions, and algorithmic tweaks to solve specific business problems.
• Data-Driven Validation: Use A/B testing and deep data analysis to validate the impact of technical changes on customer KPIs.
• On-Site Collaboration: Travel to customer sites (EMEA/AMER) to lead technical workshops and deeply understand their architectural