Top Machine Learning Jobs in Singapore

Leads digital HR platform ownership and transformation across Workday HCM, custom applications, integrations, AI automation, analytics, governance, and release management. Partners with HR, technology, risk, audit, and vendors to deliver scalable, secure, user-focused solutions. Oversees testing, deployment, compliance, change enablement, operational playbooks, and adoption while enabling workforce insights and responsible AI use in a regulated banking environment.

Develop production-ready computer vision and multimodal AI systems for interactive applications. Responsibilities include designing algorithms for detection, tracking, segmentation, classification, action recognition, and video understanding; training and optimizing models; managing annotated datasets; evaluating performance; enabling real-time or edge inference; integrating models into production; and collaborating with engineering and data teams. The role also involves research, documentation, testing, and consideration of ethical and regulatory implications.

Supports enterprise data engineering initiatives, including developing and maintaining data pipelines, preparing and validating data, improving data quality, automating manual processes, analyzing and reporting data, and exploring AI-driven solutions. Collaborates with engineering and business teams to gather requirements, translate them into data solutions, conduct testing, document work, and support continuous improvement across enterprise-scale banking data platforms.

Design, develop, deploy, and maintain automation software for factory operations including autonomous material delivery, scheduling, and equipment automation. Collaborate with manufacturing and global automation teams, own end-to-end solution lifecycle, troubleshoot production systems, use data analysis and visualization, and integrate AI-assisted tools to drive continuous improvement and operational efficiency.

Design, implement, and maintain data-driven processes and analytics solutions. Collect, cleanse, automate, and prepare data from disparate sources. Build KPIs and dashboards, apply statistical and machine learning methods, collaborate with stakeholders, perform testing and deployment, and train users to enable data-driven decision making and process improvements.

Serve as Product Owner for smart manufacturing AI initiatives to identify cost opportunities. Collaborate with data scientists, data engineers, and stakeholders to design, prioritize, implement, and measure AI/ML solutions that capture financial and operational value across procurement and front-end fabrication processes.

Design and deploy software and data-analytics solutions for Probe operations: real-time reporting, monitoring, automation of material tracking and validation, and AI-assisted efficiency improvements. Collaborate globally to identify process improvements and implement software and process changes to reduce resolution turnaround and ensure product quality.

The Engineer CE PYE will perform fault isolation, electrical device characterization, and physical failure analysis, collaborating with product teams to improve defectivity and operational efficiency.

As a CE PYE FA Engineer, you will perform fault isolation and failure analysis, collaborate with teams, and develop new techniques to enhance operational efficiency.

Lead the design, evaluation, deployment, and scaling of predictive Bio-AI models, multi-agent scientific workflows, RAG systems, and LLM applications. Build evaluation frameworks and production controls for model versioning, observability, monitoring, CI/CD, access control, auditability, and human review. Partner with scientists, bioinformaticians, and data engineers to transform scientific workflows into reliable AI capabilities and improve them through experimental feedback.

Supports semiconductor inspection products through customer demonstrations, product characterization, troubleshooting, test-plan execution, training, documentation, and collaboration with engineering, marketing, and sales teams. Investigates semiconductor manufacturing yield issues, develops inspection solutions, communicates product value, and supports alpha, beta, and ramp activities. The role requires extensive customer interaction and more than 70% travel.

Build and maintain production software for autonomous vehicle safety assurance, including monitoring systems, analytics, dashboards, data pipelines, alerts, and operational tools. Design scalable cloud systems, support production incidents, apply AI and data analytics, and collaborate with policy, regulatory, and operations stakeholders. Senior hires additionally lead architecture, establish engineering practices, mentor engineers, and guide technical direction.

Lead adoption and scaling of AI across supply chain: define roadmap, prioritize use cases, manage solution lifecycle from pilots to production, establish AI governance and playbooks, improve data readiness, drive stakeholder engagement, build community of practice, and measure business value.

Leads and scales APAC sales engineering, partnering with sales to expand enterprise markets, improve technical win rates, and accelerate adoption. Establishes presales, PoC, pilot, and demo strategies; develops managers and senior talent; evangelizes AI-powered CX through executive briefings and industry events; and provides field feedback to Product and Engineering. Requires deep CX, enterprise architecture, cloud, integration, AI, commercial, and executive communication expertise.

As a Principal Engineer, you will lead a team in defect detection and process improvement in the semiconductor industry, collaborating with various departments to drive innovation and mentor team members.

Design and implement advanced process control, automation, and analytics solutions for semiconductor manufacturing. Collaborate with process teams, apply statistical analysis and machine learning to improve yields, maintain global control applications, and deliver training while promoting continuous improvement and responsible use of AI tools.

Develops and owns end-to-end machine learning models, from ideation and prototyping through implementation. Responsibilities include building predictive models for credit scoring and recommendation engines, researching model improvements using large datasets, and advancing data science capabilities. Collaborates with analysts and machine learning engineers. Optional experience includes alternate data for credit risk and deep learning or NLP model development.

Develops and owns end-to-end machine learning models, from ideation through implementation. Responsibilities include prototyping credit-scoring and recommendation models, researching improvements using large datasets, and advancing the organization’s data science capabilities. Collaborates closely with analysts and machine learning engineers while applying quantitative, statistical, and business expertise.

Dragonfly is seeking Software Engineers for various portfolio companies, focusing on crypto technologies. Engineers of all experience levels are needed across multiple stacks.

Build privacy and anonymization systems that detect and transform PII, credentials, secrets, and other sensitive information in data used for AI training. Develop detection methods combining rules, statistical models, classifiers, and LLMs; create production data-processing pipelines; evaluate privacy risk and retained utility; and ensure robustness against schema drift, unusual formats, leakage, and adversarial re-identification.

Leads commercial performance for global and regional eCommerce markets, improving revenue, margin, EBIT, conversion, AOV, demand shaping, customer experience, and retention. Partners with market leaders to diagnose performance, recommend actions, align improvement plans, govern KPI frameworks, and ensure adoption. Leads data storytellers, establishes analytical and measurement standards, develops global playbooks, and influences senior stakeholders across a global matrix organization.

Develop and deploy AI, machine learning, GenAI, automation, and analytics solutions for Group Risk Management. Partner with business, technology, data engineering, and data science stakeholders to define requirements, deliver use cases, and influence decisions. Guide teams on model selection, GenAI cost estimation, benefit realization, governance, privacy, and responsible AI. Maintain existing solutions, promote AI adoption, and oversee analytics work across risk departments.

Build and maintain mission-critical wealth management systems for SMB clients. Design data pipelines, ETL/ELT processes, data models, warehouses, analytics, and reporting solutions using AWS, Snowflake, Mage AI, and other big data tools. Collaborate with data scientists and analysts on predictive models, statistical analyses, and machine learning algorithms. Develop software with Next.js and Python while working closely with the team and clients on financial-domain challenges.

Conduct ML research (deep learning, NLP, statistics) on large and unstructured datasets, implement algorithms in production-quality code, back-test models, and document findings to inform trading strategies.

Develop AI-driven methods for skin and scalp biology discovery, integrating multimodal biological datasets to generate predictive insights. Build Design-Build-Test-Learn workflows, analyze imaging, omics, phenotypic, screening, and experimental data, and collaborate with experimental scientists to translate biological questions into computational models. Contribute to an AI-enabled discovery platform supporting autonomous and predictive scientific research.