Senior Machine Learning Engineer

Santex is a US-based global company founded in 1999, with 26 years of experience in the software industry. Headquartered in California with offices in Córdoba, Argentina, its talent network spans over 18 countries thanks to its flexible, remote-first culture. Santex specializes in custom enterprise software development, operating through Hubs that include eCommerce, BIM, Mobility, Content Delivery, Integration, Web & Mobile Development, Cloud Computing, Artificial Intelligence (AI), Data Science, IT Consulting, and Services. The company is committed to making a positive impact across three dimensions: economic, social, and environmental.

Model Development & Deployment: Develop and deploy Machine Learning, Deep Learning, and GenAI models to enhance operational efficiency and customer experience. Continuous Improvement: Improve models by monitoring performance, conducting A/B testing, and implementing feedback loops. Architecture & Scalability: Architect and rebuild complex ML frameworks from the ground up, incorporating multi-threaded processing and distributed workloads to support scalable pipelines. Cross-functional Collaboration: Work with engineers and product managers to integrate AI solutions into production systems within a fast-paced Agile environment. Operational Excellence: Own production ML systems, participating in on-call rotations, troubleshooting incidents, and maintaining overall model reliability. Innovation: Stay updated with the latest advancements in AI to ensure our solutions remain cutting-edge.

Education: Bachelor’s or advanced degree in Computer Science, Engineering, Statistics, Mathematics, or a related quantitative field. Experience: 3+ years of experience designing, building, and operating production-scale machine learning systems. Technical Mastery: Expert-level programming in Python with extensive experience using PySpark and distributed data platforms (e.g., Databricks) for large-scale datasets. Deep Learning & Frameworks: Strong experience with Scikit-Learn, MLlib, and PyTorch, including regression, time series, clustering, and deep learning. Distributed Systems: Deep understanding of Spark execution models, partitioning strategies, shuffle optimization, and vectorized processing (Pandas UDFs). Data & Cloud: Strong SQL expertise and experience deploying ML workloads to AWS (EC2, S3, DynamoDB, Lambda). Ops: Experience implementing MLOps / LLMOps practices.

Ownership: Ability to manage strategic initiatives in a rapidly evolving QSR environment. Adaptability: Eagerness to learn and adapt in ambiguous problem spaces with a collaborative attitude. Outcome-Oriented: Focus on analyzing and visualizing data to drive continuous improvement across the business. Professionalism: Maintain transparency and professionalism in communication within the team and with stakeholders.