Top Machine Learning Jobs in the Philippines

Leads Six Sigma and operational excellence initiatives across business teams, using data-driven analysis to identify issues, improve processes, measure results, and mitigate risks. Presents findings to senior leadership, manages vendors and the Bright Idea program, delivers process training, and supports complex healthcare operations. Requires Black Belt experience, project leadership, change management, emerging technology exposure, and strong communication skills.

Leads multiple globally distributed software engineering teams delivering enterprise-scale healthcare technology. Owns engineering strategy, architecture, delivery, operational excellence, AI-enabled initiatives, reliability, and continuous improvement. Partners with Product, Operations, Business, and executive stakeholders while recruiting, coaching, and developing engineering talent. Oversees roadmaps, technical assets, platforms, performance metrics, incident remediation, and strategic technology execution across international teams.

Architects, develops, deploys, and maintains enterprise AI/ML solutions using NLP, OCR, RAG, and cloud-native platforms. The role modernizes fax and telephony workflows, mentors engineers, collaborates with product and development teams, solves complex problems, and ensures secure, scalable delivery. Responsibilities include model training, deployment, monitoring, MLOps/LLMOps practices, production support, and designing AI-driven automation.

Designs and governs scalable, secure technology solutions aligned with enterprise architecture standards. Translates business requirements into solution blueprints, application designs, integrations, data flows, and deployment architectures. Partners with stakeholders, product owners, architects, and engineering teams throughout delivery. Supports cloud migration, modernization, digital transformation, AI-enabled solutions, architecture governance, risk mitigation, technology roadmaps, and production transition while ensuring performance, resilience, security, maintainability, and long-term business value.

Develop data-driven business insights by analyzing labeling outputs, evaluating classifier performance, querying data with SQL, and creating dashboards in visualization tools. Translate complex findings into executive-ready recommendations, reporting strategies, and business decisions. Collaborate with data visualization and engineering teams to build scalable reporting systems, integrate data sources, identify risks and opportunities, and improve AI-related processes through statistical analysis, sampling, and optional A/B testing.

Analyzes catastrophe risk for reinsurance contracts using third-party catastrophe models, exposure data, and model outputs. Produces exposure reports, pricing analysis, dashboards, written summaries, and reproducible Python or R notebooks. Applies machine-learning techniques such as similarity scoring and clustering to clean and align large datasets. Requires quantitative analysis, insurance or reinsurance knowledge, strong communication, and flexibility to work extended hours during peak periods.

Designs and productionizes correlation, causality, root-cause analysis, machine learning, and LLM-based solutions using observability data. Develops scalable Java and Python services, trains and evaluates statistical and AI models, operates local AI infrastructure, and integrates tools such as Prometheus, OpenTelemetry, Loki, and Grafana. Collaborates with DevOps, SRE, application, product, and operations teams while maintaining CI/CD pipelines, containerized deployments, monitoring, testing, and documentation.

Leads data science initiatives across supply chain, manufacturing, and quality. Manages citizen data scientists, designs and deploys advanced analytics and machine learning models, develops data pipelines, integrates production ML infrastructure, and monitors model performance. The role requires translating business challenges into data-driven strategies, collaborating cross-functionally, communicating insights, and identifying AI/ML opportunities. Travel up to 20% is required.

Drive adoption and value realization of Neara’s digital-twin platform among Japanese utility customers. Responsibilities include customer engagement, platform enablement, training, ROI and business-case development, stakeholder management, solution expansion, and collaboration with Sales, Deployment, Product, and Engineering teams. The role requires translating customer strategic goals into technical solutions and measurable business outcomes, with regular customer and team travel in Japan and internationally.

Build and deploy engineering-grade digital models for Japanese utility clients by integrating GIS, LiDAR, and other data sources. Configure platform projects, create dashboards, resolve model inaccuracies, support technical delivery and pre-sales, and collaborate with product and engineering teams on platform improvements and Japanese localization. The role requires customer-facing communication in Japanese, global collaboration in English, mentoring, and occasional travel to customers and international offices.

Leads customer threat intelligence enablement, technical onboarding, alert tuning, managed intelligence services, deep-dive investigations, and executive protection analysis. Develops Python, PowerShell, and Bash automation, SIEM integrations, SOAR playbooks, AI agents, machine learning models, LLM tools, and automated reporting systems. Analyzes platform adoption and customer behavior to improve monitoring quality, engagement, retention, and security outcomes.

Lead payment lifecycle operations, including daily processing, reconciliations, investigations, exception management, and performance tracking. Resolve control and production breaks, manage operational risk, support fraud prevention, and escalate threats or delays. Partner with stakeholders on automation, process improvements, and client experience enhancements while translating business priorities into execution plans and measurable outcomes.

Lead product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

Own full-cycle recruiting for senior research, machine learning engineering, and infrastructure roles. Responsibilities include proactive sourcing, role definition, interview design, offer strategy, candidate closing, pipeline analytics, recruiting process development, and mentoring teammates. The role partners closely with founders and technical leaders, requires strong technical recruiting experience, and is based four days per week in San Mateo, California.

Leads electrical energy studies, energy audits, power quality assessments, and system evaluations for industrial customers across Southeast Asia. Analyzes field measurements and operational data, identifies efficiency and reliability improvements, quantifies business benefits, prepares technical reports and proposals, and presents recommendations to customers. The role also develops energy management, process control, asset monitoring, and digital solution opportunities while supporting sales, follow-on projects, and regional capability development.

Develops AI-powered procurement analytics, machine learning models, automation tools, dashboards, and integrated reporting solutions. The role analyzes procurement data to improve sourcing, KPI tracking, cost optimization, vendor performance, and risk mitigation. Responsibilities include collaborating with stakeholders, managing digital projects, ensuring data integrity, and supporting adoption through training materials, user guides, and testing.

Supervise a private equity fund accounting team of up to four accountants. Responsibilities include capital calls and distributions, investor allocations, IRR reporting, waterfall calculations, NAV reviews, financial statements, reconciliations, client reporting, workflow improvements, and client transitions. The role also involves resolving escalated accounting inquiries, maintaining controls, supporting client relationships, and coaching junior employees.

Designs, develops, and maintains financial business intelligence, data integration, dashboards, data warehouses, ETL processes, and SQL-based reporting solutions. Partners with Finance, Accounting, IT, and business stakeholders to translate reporting needs into reliable technical solutions, troubleshoot data issues, support management reporting, and improve data quality. The role also drives automation, standardization, continuous improvement, and analytics innovation across the Americas Controllership organization.

Own and scale machine learning data infrastructure for advertising systems, including high-throughput feature pipelines, real-time streaming, embeddings, feature serving, and deep learning model training pipelines. Improve latency, reliability, throughput, monitoring, testing, and CI/CD while partnering with data science, modeling, product, and infrastructure teams. The role involves designing resilient distributed systems and delivering measurable improvements to model performance and platform scalability.

Maintains, optimizes, and supports enterprise applications and infrastructure. Troubleshoots application issues, implements software deployments and configuration changes, supports onboarding and decommissioning, and contributes to systems recovery. Configures packaged software, develops modification and interface requirements, coordinates solutions with technical teams and vendors, executes change requests, mentors junior staff, and provides strategic recommendations.

Reviews financial services regulations and exchange rulebooks, assesses regulatory changes, and maintains auditable review records. Validates machine-learning topic classifications, provides labelled feedback, and helps maintain regulatory taxonomies. Compares platform rulebooks with authoritative sources to identify structural errors, missing provisions, duplication, and versioning issues. Performs quality assurance against development requirements and collaborates with product, engineering, and software teams to process new regulations and improve the service.

Deploy and integrate AutoScience into customers’ machine learning infrastructure. Responsibilities include understanding ML systems, writing production Python, debugging distributed systems and pipelines, translating modeling problems into experiment environments, and owning deployments through production model improvements while collaborating with customers, researchers, and engineers.

Write technically accurate case studies, articles, product explainers, launch materials, and social content for AI engineers, researchers, and technical leaders. Interview customers, partners, and internal experts; learn HUD’s platform and workflows; explain technical products and research clearly; support founder communications; manage content from ideation through publication; and improve content using reader and go-to-market feedback.

Develop and support scalable full-stack business applications across the software development lifecycle. Responsibilities include building front-end interfaces, back-end services, APIs, databases, integrations, and Power Platform solutions; writing tested and secure code; troubleshooting; participating in code reviews; and applying CI/CD and DevSecOps practices. The role collaborates with cross-functional teams on digital transformation, prototyping, technical investigations, and continuous improvement.