Top Machine Learning Jobs in the Philippines

Design and maintain low-latency, high-throughput feature pipelines (batch, streaming, point-in-time), optimize large-scale deep learning training pipelines, integrate embeddings/LLMs for real-time serving, and establish monitoring, testing, and CI/CD standards. Partner with modeling, data science, and infrastructure teams to improve serving latency, training throughput, reliability, and scalability of ad-serving systems.

Design, build, and deploy scalable ML models and pipelines from research to production. Own feature engineering, model tuning, real-time prediction, MLOps automation, monitoring, and lifecycle management to solve visibility, forecasting, and freight audit problems.

Lead end-to-end ML lifecycle: research SOTA architectures, fine-tune foundation models, design data pipelines, deploy models with MLOps (Docker/Kubernetes/CI-CD), build APIs, implement model governance, and collaborate within cross-functional agile squads.

Own end-to-end ML model lifecycle from EDA and feature engineering to production deployment and monitoring. Collaborate with cross-functional teams to identify modeling opportunities, evaluate performance and business impact, integrate new data sources, and ensure scalable, observable production systems.

This role involves leading pre-sales engagements, designing scalable solutions for customers, and evaluating ML model performance to enhance training efficiency.

Develop LLM-based systems for code generation, document understanding, and context synthesis. Build agentic pipelines and evaluation frameworks, collaborate with customers and founders to define product and engineering roadmap, and help shape engineering culture and tech stack.

Design, implement, and optimize production-ready machine learning models and pipelines. Collaborate with data, software, and product teams to integrate ML solutions. Perform data preprocessing, feature engineering, EDA, training, validation, deployment, and monitoring. Maintain environment setup, scalable APIs, and database architecture. Document work and stay current with ML research and best practices.

As a Data Scientist at Dyneti, you will track fraud trends, translate insights into product improvements, and build impactful solutions to enhance fraud prevention.