• Take technical ownership of core components of recommendation and personalization systems (retrieval, ranking, evaluation). • Design and evolve two-tower / embedding-based retrieval models and downstream rankers. • Drive architectural and modeling decisions with a strong understanding of trade-offs between model quality, system complexity, latency, and cost. • Define and promote best practices for ML system design, experimentation, evaluation, and deployment. • Review ML designs, pipelines, and code with a focus on correctness, maintainability, and production readiness. • Act as a technical point of reference for ML-related decisions within the team. • Develop, train, and improve ML models for retrieval and ranking use cases. • Work with embedding-based deep learning models and classical ML approaches. • Perform in-depth data analysis, feature exploration, and systematic error analysis. • Build reproducible experiments and robust offline evaluation pipelines. • Optimize models for both offline metrics and online business KPIs. • Design and operate batch and real-time training and inference workflows in a cloud environment, with awareness of scalability and cost trade-offs. • Design, run, and analyze offline experiments and online A/B tests. • Own ML components in production, with a strong focus on reliability, observability, and safe iteration. • Monitor model performance and data quality in production. • Collaborate on scalable training and serving infrastructure for ML systems. • Participate in incident analysis related to ML systems and contribute to root-cause analysis and long-term fixes. • Design ML systems with failure modes in mind, including fallbacks and graceful degradation. • Work closely with Data Engineering on data pipelines and feature generation. • Partner with Product and Analytics to translate business goals into clear ML objectives and success metrics. • Act as a technical mentor for ML engineers, providing guidance on modeling, experimentation, and production ML. • Provide constructive feedback through code reviews and design discussions, supporting the growth of the team.

• Design, build, and optimize machine learning models to enhance Xometry’s platform and business operations. • Analyze large datasets to extract meaningful patterns and insights. • Collaborate with cross-functional teams to integrate machine learning models into production systems. • Learn and apply best practices in model evaluation, performance tuning, and deployment. • Influence technical direction by identifying opportunities to improve modeling approaches, data quality, and system architecture. • Work across teams to ensure machine learning solutions are explainable, maintainable, and aligned with business goals. • Help bridge the gap between research and production, ensuring models perform just as well in the real world as they do in notebooks. • Gain exposure to cutting-edge machine learning frameworks, tools, and techniques used in the manufacturing industry.

• Educate and evangelize AI to boost productivity across all internal groups • Build scalable, high-availability models from claims, contracts, employer and price transparency data to create intelligence and handle real-time inference requests • Manage and optimize the underlying cloud infrastructure for both large-scale model training and high-throughput serving environments • Implement advanced monitoring and alerting systems to track model performance, data drift, and prediction latency in production • Establish processes for model versioning, lineage tracking, and rollback capabilities to ensure operational integrity and auditability • Collaborate directly with the Data Science team to containerize model code, optimize models for production serving efficiency, and transition models from research to production-readiness • Automate all aspects of the model lifecycle, from data ingestion and feature engineering to deployment and retraining triggers

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description To be part of this fast–growing fintech and join the team responsible for implementing the data strategy for the company, and delivering tangible results to support and implement Baubap’s mission. As a Senior Machine Learning Engineer in Baubap you will work closely with the business and operational units, understand the problems that we want to solve, propose and implement data- and Machine Learning-driven solutions. In addition, you will be able to measure and communicate the impact of Machine Learning models on business. - Successfully develop and implement machine learning models that optimize financial processes, resulting in a significant improvement in efficiency and profitability. - Improving and building new ML models into production for new project lines. - Generate new insights to be able to have better understanding of the clients using ML. - Research and have fast insights in order to make improvements to the actual ML models implemented in Baubap. - Optimize and refine models to achieve desired accuracy and efficiency. Day to day tasks include: - Understand, propose, design, implement, and bring Machine Learning solutions into production to address business problems. - Create POC’s researching exhaustively the viability to find new features, data sources and/or algorithm optimizations. - Propose creative new initiatives that can improve our Credit Risk Models. - Coordinate with Data Engineers to ensure smooth and successful implementation of pipelines for the new features. - Collaborate with engineering and product teams to integrate and optimize machine learning algorithms within the company's applications and systems. - Evaluate, enhance, and maintain existing machine learning models, and propose continuous improvements. Qualifications - Proficiency in Python. - Proficiency in MySQL. - Strong Experience with the complete Machine Learning Pipeline (Feature engineering, ML model training, evaluation and monitoring). - Experience in projects using AWS like S3, EC2, GLUE, Lambda. - Experience in projects using NLP, Face Recognition, ML recommenders, Image Pre-processing. Benefits - Being part of a multicultural, highly driven team of professionals. - 20 vacation days / year + 75% holiday bonus (Prima Vacacional). - 1 month (proportional) of Christmas bonus (Aguinaldo). - Food vouchers. - Health & Life insurance. - Competitive salary.