• Construir y optimizar modelos de regresión, clasificación, clustering y series temporales. • Desarrollar pipelines de *feature engineering* y realizar evaluación y ajuste de modelos. • Desplegar modelos en entornos productivos (APIs, Docker). • Colaborar con equipos de datos y negocio para transformar necesidades en soluciones ML.

• Develop, train, and iterate on ML models for retrieval and ranking use cases. • Work with embedding-based deep learning models and classical ML approaches. • Perform data analysis, feature exploration, and systematic error analysis to improve model performance. • Build and maintain reproducible experiments and robust offline evaluation pipelines. • Optimize models for both offline metrics and online business KPIs. • Support and improve ML components in production, focusing on reliability and observability. • Design and operate batch and real-time training and inference workflows in a cloud environment. • Monitor model performance and data quality to detect drift or degradation. • Collaborate on scalable training and serving infrastructure to ensure low-latency performance. • Participate in incident analysis and contribute to long-term fixes for ML systems. • Assist in designing, running, and analyzing offline experiments and online A/B tests. • Work closely with Data Engineering to build efficient data pipelines and feature sets. • Participate in design reviews and code reviews to ensure maintainability and production readiness. • Partner with Product and Analytics to understand business goals and translate them into technical ML tasks.

Summary: Join Temporal, a leading Solana native Research & Development firm, as a Machine Learning Engineer. Contribute to cutting-edge projects by designing, training, and deploying predictive models for adversarial systems while building high-performance ML infrastructure. Stay at the forefront of technological advancements by engaging with the latest research and contributing to open-source development. Responsibilities: - Design, train, and deploy predictive models for adversarial systems - Build and maintain high-performance ML infrastructure that scales from research prototypes to production systems - Stay on the frontier: read new papers, reproduce results, experiment with hardware, and contribute to open source Required Skills: - Deep foundations in optimization, statistics, and linear algebra - Expertise in ML frameworks (ideally PyTorch) and strong software engineering skills - Experience building real-time or distributed ML systems - Curiosity, independence, and a track record of original contributions (research, open source, competitions, patents) Compensation: $200,000–$300,000 base salary, equity package, and discretionary bonus

• Architect, design and develop AI/ML solutions to solve novel business problems • Implement both traditional machine learning models and modern AI techniques • Collaborate closely with product managers, engineers, data scientists, and business stakeholders • Build robust, scalable, and maintainable AI systems with a focus on accuracy, robustness, fairness, and explainability • Develop and execute rigorous model evaluation frameworks to assess performance and resilience • Transition AI prototypes into production-ready systems • Contribute to documentation, governance, and compliance processes