• Design, build, and iterate on VLM-based pipelines that generate high-quality labels and annotations at scale, including prompt engineering, fine-tuning, and evaluation • Determine which signals, frames, metadata, and contextual features should be sent from edge devices to improve VLM accuracy and reduce ambiguity • Collaborate with Embedded Systems and Hardware teams to define device-side preprocessing and data-forwarding strategies that balance bandwidth, latency, and model performance • Collaborate with Data Science to build robust evaluation frameworks to measure label quality, model accuracy, and regression detection • Benchmark and integrate commercial and open-source VLMs, staying current on the fast-moving landscape of vision-language capabilities

• Own the lifecycle of our computer vision models, including training, evaluation, deployment, and iteration • Improve model performance in real-world conditions (noise, edge cases, data drift) • Design and improve data pipelines that process thousands of miles of sidewalks and millions of images • Lead architectural decisions for handling hundreds of terabytes of geospatial and visual data, including storage layout, pipeline reliability, and inference performance • Set and evolve best practices around deployment, observability, system reliability, and scalability • Act as a senior individual contributor and mentor, helping raise the technical bar across the engineering team • Work closely with the CEO and operations team to turn business needs into clear technical priorities

• Design and implement end-to-end ML solutions from experimentation to production; • Build scalable ML pipelines and infrastructure; • Optimize model performance, efficiency, and reliability; • Write clean, maintainable, production-quality code; • Conduct rigorous experimentation and model evaluation; • Troubleshoot and resolve complex technical challenges. • Mentor junior and mid-level ML engineers; • Conduct code reviews and provide constructive feedback; • Share knowledge through documentation, presentations, and workshops; • Collaborate with cross-functional teams (DevOps, Data Engineering, SAs); • Contribute to internal ML practice development. • Stay current with ML research and emerging technologies; • Propose improvements to existing solutions and processes; • Contribute to the development of reusable ML accelerators; • Participate in technical discussions and architectural decisions.

• Diseñar e implementar soluciones basadas en LLMs (GPT, Claude u otros). • Construir agentes y flujos conversacionales, gestionando contexto, prompts y memoria. • Integrar servicios de IA a través de APIs (OpenAI, Anthropic, Azure AI). • Trabajar con datos estructurados y no estructurados para enriquecer las capacidades del sistema. • Iterar sobre modelos y prompts en base a feedback de usuarios y métricas. • Optimizar performance, calidad de respuestas y costo de inferencia. • Asegurar buenas prácticas de seguridad, privacidad y uso responsable de IA. • Colaborar con equipos de producto y negocio para definir casos de uso y soluciones.