Staff Engineer - Recommendations

Triwill Group; Spain (TX); On-site; USD 81,000 - 139,000; Full time

Job summary

Triwill Group is looking for a Staff Engineer - Recommendations based in Spain. You will design data pipelines, scale ML-powered personalization, and work across data engineering, backend, and product teams.

You will translate user behavior signals into relevant content recommendations, collaborate with data scientists, and help build a distributed, cloud-based platform serving millions of interactions daily.

Qualifications

• Experience designing scalable data pipelines and data platforms.
• Experience shaping recommendation or personalization systems.

Responsibilities

• Build and evolve data pipelines and ML-enabled features for personalized discovery.
• Collaborate with data scientists, engineers, and product teams to translate signals into recommendations.
• Design systems across cloud infrastructure, APIs, and distributed processing at scale.

Skills

Data engineering

Backend development

ML / Machine Learning

Personalization

Job description

Description:

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Engineer - Recommendations based in Spain.

This role focuses on building the data and machine learning foundations that power personalized discovery and social experiences at scale.
You will help evolve recommendation systems that connect users with relevant content, communities, groups, and events.
Working across data engineering, backend development, and personalization, you will design systems capable of processing large volumes of platform-generated data.
You will collaborate closely with data scientists, product managers, and engineers to turn behavioral signals into meaningful user experiences.
The role offers significant technical scope across cloud infrastructure, data pipelines, APIs, distributed systems, and ML-enabled applications.
You will also help advance personalization from simple heuristics toward increasingly sophisticated, data-driven recommendations.
This is an opportunity to make a direct impact on a highly interactive consumer platform within a distributed, collaborative engineering environment.