FORD MOTOR COMPANY / GLOBAL TECHNOLOGY BUSINESS CENTER
Machine Learning Software Engineer
IPTSE CAE
– México City
- Built and maintained Python automation pipelines for CAE analytics and reporting, turning raw simulation outputs into actionable metrics.
- Designed reusable, well-documented code following PEP-8 and clean-code practices; delivered production-ready modules for internal users.
- Implemented workflow automation and integrations (Python/JS) to connect data sources, tools, and visualization outputs.
- Owned feature computation, validation, and performance-aware visual outputs for engineering insights (2D/3D + interactive views).
- Version-controlled delivery using Git/GitHub roadmaps for planning, issue tracking, and release coordination.
- Produced technical documentation and notebooks to communicate results, methods, and usage to stakeholders.
- Contributed to UX/UI design flow (Figma) for internal tools, improving usability and adoption.
- Owned end-to-end, production-grade systems: data ingestion, feature computation, validation, and delivery through usable engineering tools.
- Designed pipelines that ingest, transform, and analyze CAE/simulation datasets, exposing insights via interactive and scalable interfaces.
- Built Python-based data pipelines and workflow automations; improved reliability with structured logging, reproducible runs, and clear error handling.
- Delivered container-friendly services and modules, with a builder mindset focused on system ownership rather than isolated scripts or models.
- Applied geometry, spatial reasoning, and aggregation strategies from CAE tooling to build performance-aware visualizations and analytics outputs.
- Implemented feature computation and metrics layers to support ML-ready datasets and scalable analytics (from raw data to consumable features).
- Collaborated cross-functionally, translating requirements into clean, reusable, well-commented code and maintainable architectures.
- Maintained and evolved existing production tools, refactoring when needed to improve clarity, extensibility, and performance.
- Tested and validated outputs against trusted references; ensured correct behavior before production release.
- Created technical documentation and Jupyter-based presentations to communicate systems, results, and best practices.
- Proactively explored scalable patterns for data systems (pipelines, services, visualization), continuously strengthening ML + data engineering capabilities.