Top Machine Learning Jobs in Davao City

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.

Build and operate production-grade AI data pipelines and financial knowledge services for equities content. Responsibilities include processing text, tables, documents, and multimedia; creating knowledge bases and search indexes; engineering RAG retrieval and citation systems; integrating LLMs and NLP models; managing orchestration, APIs, quality, cost, observability, reliability, and scalable production operations.

Bridges business needs and AI solutions by gathering requirements, assessing AI and automation opportunities, developing business cases, supporting solution design and delivery, facilitating stakeholder collaboration, assisting testing and implementation, and tracking project outcomes, benefits, reports, and continuous improvement initiatives.

Leads operational oversight for AI-powered products and platforms, monitoring performance, developing dashboards, analyzing business outcomes, and driving process improvement. The role supports AI governance, risk management, compliance, operational standards, forecasting, and scalable operating models. It also partners with engineering, product, and business stakeholders to deliver operational excellence and present strategic recommendations.

Leads business analysis for AI and automation initiatives by identifying adoption opportunities, defining requirements, developing business cases, facilitating stakeholder alignment, and supporting solution design and delivery. Monitors outcomes, value realization, governance, compliance, change management, and continuous improvement while mentoring junior analysts. Translates business objectives into technical requirements and advises executives and technical teams throughout AI transformation programs.

The AI Security Analyst will assess and mitigate risks across AI systems, machine learning platforms, and Generative AI applications. Responsibilities include threat modeling, vulnerability assessment, security monitoring, incident response, root cause analysis, access control, governance, compliance, policy development, and secure AI design reviews. The role partners with engineering, infrastructure, security, governance, and business teams to improve AI security capabilities and promote responsible AI adoption.

Owns AI product backlogs, priorities, user stories, acceptance criteria, and requirements. Translates business needs into technical solutions while coordinating stakeholders, AI engineers, product managers, and delivery teams. Supports Agile planning, product releases, adoption monitoring, continuous improvement, AI innovation, and responsible AI practices. Facilitates workshops, manages expectations, and contributes to AI product strategy and value realization.

Leads the strategy, roadmap, delivery, and continuous improvement of AI-powered products. Defines product vision, requirements, priorities, metrics, and success criteria while collaborating with executives, engineering, AI, data science, and business teams. Oversees product launches, adoption, performance optimization, stakeholder communication, and responsible AI governance. Requires strong product management, analytical, commercial, Agile, executive communication, and cross-functional leadership skills.

Supports responsible AI governance through framework development, risk and impact assessments, fairness and transparency reviews, regulatory compliance, documentation, stakeholder advisory, training, and continuous improvement. The role collaborates with technical, legal, compliance, risk, and business teams to promote ethical, accountable, and trustworthy AI adoption.

Supports enterprise AI governance by developing policies, maintaining control frameworks, assessing AI risks, monitoring regulatory compliance, and reporting governance metrics. Collaborates with legal, compliance, security, AI, and business stakeholders to promote responsible AI practices. The role also researches emerging regulations, supports audits, documents findings, and recommends improvements to governance processes.

Own the strategy, roadmap, development, delivery, and continuous improvement of AI-powered products. Translate business objectives and customer needs into product requirements, success metrics, and business cases. Partner with engineering, AI, operations, leadership, and customers to manage product lifecycles, adoption, ROI, governance, compliance, and responsible AI practices.

Supports the day-to-day operation, monitoring, reporting, governance, and continuous improvement of AI-powered platforms. Analyzes performance metrics, identifies operational issues and optimization opportunities, develops dashboards and reports, maintains procedures and documentation, and coordinates issue resolution across AI engineers, product teams, operations leaders, and business stakeholders. The role also supports AI adoption, compliance, risk mitigation, and operational standards.

Tests and validates AI-powered products, machine learning models, and Generative AI applications. Designs test strategies, executes functional, regression, and acceptance testing, validates model outputs, identifies defects, analyzes quality metrics, documents results, and supports compliance and release readiness. Collaborates with AI engineers, product managers, and business teams while improving AI testing methodologies and quality frameworks.

Develop user-facing financial AI and equity research algorithms across query understanding, document processing, entity recognition, event detection, retrieval, ranking, and quality evaluation. Build datasets, labeling systems, benchmarks, and production evaluation frameworks. Apply large language models, NLP, machine learning, reinforcement learning, knowledge graphs, and multimodal methods while collaborating with engineers to deploy and continuously optimize financial data and knowledge systems.

Designs and develops content governance platforms, anti-abuse tools, rule engines, and scalable governance pipelines. Integrates recommendation systems, implements content takedown policies, and builds capabilities for content review, risk detection, policy enforcement, and effectiveness analysis. The role addresses spam, fraud, traffic diversion, low-quality content, and black- or gray-market activity while collaborating with product, operations, algorithm, and business teams.

Leads analysis, mining, modeling, and visualization of large-scale transactional, operational, and customer data. Partners with compliance teams to prioritize analytical use cases, support regulatory requirements, improve business efficiency, and translate complex findings into actionable recommendations. Oversees multidisciplinary analysts, manages stakeholder data requests, assesses business changes, and develops analytical systems using machine learning, big data technologies, and visualization tools.

Own an AI-native stockbroking product vertical end-to-end, including strategy, metrics, roadmap, technical design, delivery, and launch. Partner with engineering on APIs, data flows, ledgering, reconciliation, security, scalability, and failure handling. Incorporate KYC, transaction monitoring, limits, privacy, licensing, regulatory approvals, and third-party financial rails. Apply AI with appropriate evaluation, safeguards, human oversight, and reliability controls while aligning engineering, design, compliance, operations, treasury, and finance.

Recruit, onboard, activate, and grow managed service provider partners across assigned markets. Own recurring revenue growth, product adoption, cross-sell, partner tier progression, and consumption-based sales. Deliver partner onboarding, business reviews, certifications, and sales enablement while managing high-volume pipelines, forecasting, deal registration, distributor alignment, and presales collaboration in Salesforce.

Leads finance transformation by owning core financial processes, modernizing ERP and planning systems, deploying AI and automation, partnering with engineering and data teams, redesigning workflows, establishing controls, and driving organizational adoption. The role requires senior finance leadership, strong systems expertise, hands-on AI transformation experience, process governance, and the ability to translate between finance and technology.

Support product development for homepage, user center, AI-generated interfaces, market pages, onboarding, personalization, and recommendation experiences. Coordinate with engineering, design, data, and business teams; organize requirements; track metrics; support experiments; analyze feedback; and help resolve issues. The role is part of a 3–6 month early-career accelerator program focused on blockchain products and AI-driven user experiences.

Lead supply chain business analysis and consulting across presales, RFPs, client discovery, requirements gathering, process mapping, fit-gap analysis, solution design, and implementation support. Translate business needs into functional requirements and scalable technology solutions using supply chain platforms, analytics, and AI. Facilitate stakeholder workshops, define KPIs and analytics requirements, support testing and deployment planning, and manage client relationships across business and technology teams.

Analyze large-scale transactional, operational, and customer datasets; build visualizations, machine learning models, and experiments; translate complex findings into clear recommendations for operational teams and executives; and apply analytical software, databases, visualization tools, and programming languages in a fast-paced blockchain environment.

This internship involves developing AI-driven solutions, testing features with LLMs, building prototypes, collaborating with engineers, and exploring AI capabilities.

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.