Top Machine Learning Jobs in the Philippines

Manage delivery of multiple concurrent software, data, automation, and AI programs from intake to adoption. Coordinate distributed engineers and analysts across time zones, run delivery cadence, align business partners, maintain portfolio governance, surface risks with mitigation, and continuously improve delivery processes focused on shipped outcomes.

Lead delivery of multiple concurrent software, data, and AI programs from intake to deployment. Coordinate distributed engineers and analysts, run delivery cadence, align business partners, manage risks and dependencies, and improve portfolio governance and PM practices to ensure adoption and shipped outcomes.

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.

Responsible for driving revenue by acquiring new B2B customers, conducting technical discovery calls, managing sales pipelines, and collaborating with global teams.

Lead strategy and execution for fraud risk management across the platform. Develop detection and prevention frameworks, optimize transaction screening, partner with Compliance, Data Science, Engineering and Product, oversee KPIs and vendor costs, lead a risk strategy team, and pilot AI/LLM-driven tools to enhance detection while enabling business growth and regulatory alignment.

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.

Design and implement robotic automation systems, collaborating with machine learning engineers for large-scale deployment and testing of robotics solutions.

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.

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.

Develop product positioning, messaging, and go-to-market strategy for Autoscience's AI research systems. Create sales enablement and customer-facing materials, conduct market and competitive research, collaborate with research, engineering, sales, and leadership, and communicate product value to customers, partners, and the AI community.

Develop and implement AI systems for automated research, collaborate on production-ready models, publish research, and stay updated on AI trends.

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.

Manage requests for new ServiceNow reports and dashboards, resolve dashboard/reporting incidents, train users on dashboards, build executive KPIs and scorecards, engage stakeholders to gather requirements, and apply AI/Generative AI for reporting automation and insights.

Lead end-to-end research applying advanced ML/DL (transformers, GNNs, RL, ensembles) to generate alpha across digital asset markets, build production-grade pipelines, prevent leakage/overfitting, analyze microstructure and on-chain data, and mentor junior researchers.

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.

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.

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 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.