Top Machine Learning Jobs in Quezon City

Manage overall NAV calculation for credit-based funds, client relationships, fund accounting team, and ensure timely delivery of NAV calculations while supporting audits and training.

Responsible for overseeing fund accounting tasks, including NAV calculations, reconciliations, and mentoring team members while ensuring compliance with procedures.

Own data quality and root-cause analysis for prediction issues: detect anomalies, trace sources, fix processes, automate QA/reporting with Python and SQL, maintain dashboards, and partner with Data Science, Engineering, Product, and Commercial teams to improve prediction performance and customer-facing analytics.

Lead engineering projects, automation and digitization, new product qualification, and machine/process reliability. Manage budgets, timelines, root-cause troubleshooting, maintenance strategy, and integration of advanced manufacturing technologies. Mentor and develop engineering team, ensure safety and compliance, and coordinate with regional/global stakeholders to drive productivity and operational excellence.

Design and deploy AI/ML models and automation to improve procurement analytics, integrate data from ERP and BI tools, build dashboards, lead digital projects, and drive stakeholder adoption and training.

Design, develop, and manage large-scale search and content recommendation systems. Apply ML-based personalization to improve content matching and distribution. Lead core recommendation module development, define success metrics, and collaborate with content, product, and business teams to drive AI product upgrades and better user and business outcomes.

Design, build, and manage large-scale search and content recommendation systems using machine learning and personalization techniques. Collaborate with content, product, and business teams to define success metrics, drive AI product improvements, and maximize content value and user impact.

Early-career Performance Engineer in the Binance Accelerator Program. Perform hands-on performance testing and tuning, design AI-powered test strategies using LLMs and ML, build AI-driven anomaly detection and bottleneck analysis tools, research new AI technologies, and improve reliability of large-scale distributed systems.

Design, back-test and implement pricing, risk management, and agency trading strategies; collaborate with developers to integrate models into the trading platform; participate in product ideation, modeling, parameter tuning, and risk evaluation.

Manage privacy engineering initiatives by conducting assessments, developing compliance solutions, and integrating AI technologies, ensuring regulatory adherence and risk mitigation.

Lead design, implementation, and optimization of fraud detection rules and ML models. Analyze large datasets for emerging fraud, monitor KPIs, manage vendor/tool integration, lead incident investigations, and ensure quality assurance and documentation of fraud strategies.

Early-career role analyzing PB-scale on-chain, transactional and customer data. Perform data engineering, ETL, querying, build visualizations and ML models, translate insights for teams, manage reporting environment, secure confidential data, and troubleshoot reports/databases.

Analyze PB-scale trading, transactional, operational and customer data; build visualizations, ML models and experiments; translate findings into actionable recommendations for trading, operations, product and executives in a fast-paced environment.

Responsible for driving adoption of the FICO Platform by educating clients on its capabilities, managing sales targets, and building strategic relationships with senior executives, particularly in financial institutions.

The role involves leading strategic client accounts, managing complex deal structures, and collaborating with internal teams to create tailored solutions. Responsibilities include business growth, client advocacy, and market leadership while maintaining relationships with senior stakeholders in financial institutions.

Lead design and delivery of analytics, automation, and AI-enabled data solutions to improve operational efficiency across Asia Pacific. Build data pipelines, models, dashboards, desktop apps and Excel add-ins; advise stakeholders, manage projects, establish standards, and coach junior team members.

Architect and improve autonomous AI optimization algorithms for a marketing platform. Analyze A/B tests, integrate AI models, develop multi-touch attribution, expand advertising-channel capabilities, reduce inference costs and latency, incorporate customer feedback, and maintain reliable, continuously improving optimization loops.

The Sales Manager will drive revenue growth and expand market share in the Philippines through strategic sales initiatives, client relationship building, and leading sales cycles.

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 and grow BioCatch accounts in the Philippines (primarily banking/financial services). Build strategic account plans, prospect and close new business, upsell and cross-sell, collaborate with APAC commercial teams, manage pipeline/forecasting, represent BioCatch at events, and drive customer satisfaction and technology adoption.

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.

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.

Design, automate, and manage end-to-end ML pipelines and scalable infrastructure on Azure. Build CI/CD with GitHub Actions, containerize models with Docker and AKS, monitor model lifecycle and data drift, implement cost controls, and collaborate with data science, DevOps, and IT to deploy resilient, production-grade ML solutions.