2 months ago
Rome, ItalySenior
Responsibilities
- Design, build, deploy, and monitor production machine-learning models and pipelines, including audience segmentation and affinity models.
- Build and maintain high-volume feature engineering and data pipelines using Spark, Databricks, Python, and SQL across tables with tens of billions of rows.
- Implement MLOps capabilities including model registries, offline and online evaluation, experiment tracking, and data/model drift and quality monitoring.
- Integrate audience models with the Audience API, User Profile API, Kafka Topics, DV360, Braze, TTD, and other downstream activation services.
- Design offline evaluations and A/B tests, tune models and hyperparameters, and identify new data signals such as Store Trip Data.
- Operate production backend and data systems, including participation in the on-call rotation.
- Ensure modeling and data-pipeline initiatives comply with GDPR, CCPA, and consent-management requirements.
- Collaborate with Product Analytics, Marketing Science, Data Engineers, and Software Engineers to support revenue objectives.
- Use AI-assisted development tools such as Cursor and Claude Code to improve engineering throughput.
Requirements
- Bachelor’s degree in Computer Science, Mathematics, Physics, Engineering, or a related quantitative field.
- Proven end-to-end production machine-learning experience covering feature engineering, model training, evaluation, deployment, and monitoring.
- Strong proficiency in Python, Spark, SQL, and Databricks, with experience building distributed data pipelines at scale.
- Strong software engineering fundamentals and experience owning and operating production backend and data systems.
- Experience with model lifecycle tools, model registries, production monitoring, and MLOps practices such as MLflow or equivalent.
- Solid statistics, model selection, hyperparameter tuning, and experiment-design skills, including A/B testing.
- Hands-on experience using AI development tools such as Cursor or Claude Code.
- Fluent written and spoken English, with availability overlapping North American hours from 9 a.m. to 12 p.m. EST.
- Preferred qualifications include ad-tech or programmatic advertising experience, privacy engineering knowledge, deep Databricks platform administration, and familiarity with OpenAI, Gemini, and embedding research.
Benefits
- Fixed gross annual salary of €50,000–€70,000.
- Flexible hybrid work model with full remote availability from Europe; offices are available in Milan, Barcelona, and Sofia.
- Learning opportunities, regular feedback sessions, autonomy, and flexible working practices.
- Modern central offices with snacks, coffee, and ergonomic setups.
- Team events including offsites, happy hours, company parties, and celebrations.
- Necessary equipment is provided for remote work and workspace setup.
- Additional country-specific benefits may be available under local contracts and practices.
Tech Stack
Categories
About ShopFully
ShopFully builds a “drive-to-store” digital advertising platform that connects online shoppers with local retailers’ offers through its apps, websites, and a large publisher network. Retailers and brands use its AI-based targeting and ad products to promote weekly deals and measure in-store impact, paying for media and campaign services. Founded in 2012 and headquartered in Milan, it operates across 25 countries and reports connecting around 200 million shoppers for hundreds of retail clients.