Top Machine Learning Jobs in Singapore

Lead end-to-end AI-powered risk strategy for crypto buy and sell products, including fraud prevention, transaction controls, decision engines, review workflows, and risk policies. Analyze large-scale data, identify fraud patterns, optimize models and thresholds, and transform manual reviews into intelligent AI Agent workflows. Partner with Engineering and Risk Operations to build scalable systems, improve automation, reduce review costs, and minimize user friction.

Provides technical consulting for AML, sanctions screening, and third-party risk solutions across APAC. Leads RFPs, sales engagements, client implementations, API integrations, data pipelines, analytics, reporting, and risk decisioning initiatives. Advises customers on data architecture, modeling, transformation, and enrichment while serving as a technical SME and customer advocate. Partners with sales, product, and delivery teams, presents at industry events, and monitors regulatory, technology, and competitive developments.

Design, integrate, deploy, and maintain secure OT/IoT systems spanning hardware, software, cloud platforms, and connected devices. Develop web and backend applications, integrate OT protocols and hardware interfaces, optimize cloud-native services, implement CI/CD and monitoring, protect data, support machine learning integration, improve user workflows, and maintain technical documentation. Collaborate with stakeholders on architecture, product roadmaps, governance, and tender evaluations.

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.

Lead credit risk management for customer accounts, enhancing profitable growth through innovative strategies and compliance with regulations. Oversee talent development and integrate advanced data capabilities.

Analyze large-scale on-chain data to detect anomalous transactions, develop detection mechanisms and risk monitoring frameworks, apply supervised and unsupervised ML, identify malicious accounts via community-discovery algorithms, and design integrated on-chain/off-chain prevention and control systems.

Define and execute the product roadmap for AI-driven on-chain monitoring tools. Collaborate with cross-functional teams, analyze market and customer feedback, prioritize features, ensure timely delivery, and communicate progress to stakeholders to enhance blockchain security offerings.

18-month graduate program with two 9-month rotations in quantitative analysis, market risk, research or trading analytics. Build quantitative models, analyse market data, develop forecasting and optimisation tools, apply programming and statistical techniques, collaborate with traders and risk teams, and present findings. Structured learning, mentorship, and potential international rotation included.

Build assistive 3D tooling using geometry methods, implicit representations, neural rendering, and generative models to accelerate cleanup, retopology, UV/texturing, and other game-asset pipeline steps. Partner with artists, define evaluations for 3D quality and game-readiness, and ship production-ready ML systems under runtime constraints.

Design and deliver ML tools that automate animation tasks (retargeting, cleanup, foot-locking, rig workflows). Partner with animators and tech artists, evaluate motion quality and time savings, and integrate production-ready motion generation and control models within runtime constraints.

Design, build, and evolve internal ML platform capabilities, self-service workflows, and automation to improve developer productivity, reliability, observability, and deployment safety. Troubleshoot model-serving and GPU workloads, define SLOs/SLIs, run resilience testing, integrate AI-assisted engineering tools, and lead incident response and operational improvements while establishing governance and guardrails for AI-native workflows.

Lead development and optimization of semiconductor fabrication processes, create and review process recipes and technical data, establish SOPs and FMEAs, integrate equipment/material capabilities, drive continuous improvement, mentor engineers, collaborate cross-functionally and with vendors, and contribute to patents and publications while ensuring safety and compliance.

Develop and verify automation software for high-volume manufacturing equipment. Collaborate with vendors and cross-functional teams to define requirements, perform FAT/SAT commissioning, and execute IQ/OQ/PQ qualification. Support PLC integration, software testing, and validation to meet regulatory and quality standards.

Lead and mentor a logistics analytics team to deliver insights on fleet performance, delivery efficiency, and cost optimization. Shape analytics roadmap, design performance metrics and AI data products, apply decision science and operations research, manage end-to-end analytics execution, and present executive-ready recommendations to drive measurable improvements.

Design, build, and maintain backend APIs and services for a real-time supply chain visibility platform. Own system architecture, real-time event ingestion, data normalization, ML integration, monitoring, and production debugging while collaborating with Product and Data Science to deliver scalable, high-availability features.

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.

Enterprise sales role selling FICO's optimization and decisioning platform into financial services. Build C-level relationships, develop pipelines and joint business cases, lead complex deal structuring and negotiation, coordinate pre-sales and professional services, manage forecasts and territory targets, and travel to client meetings (25-50%).

Lead technical product development for AI-powered assistant systems: define end-to-end requirements, translate model capabilities into product decisions, drive trade-offs across quality/latency/cost/reliability, design evaluation frameworks, work closely with ML/backend/mobile engineers, and own product quality, predictability, and user trust.

Three-month onsite internship developing and evaluating video-generation machine learning systems. Projects may involve reward modeling, inference efficiency, multimodal data pipelines, distributed training infrastructure, large-scale experimentation, and evaluation tooling. Interns own a defined research or engineering project with senior mentorship and may contribute to conference publications or product-adjacent work.

Technical co-founder (CTO) to define architecture, validate scientific hypotheses, build hands-on PoC/MVPs, lead the technical team, manage IP strategy, and communicate technology to investors and partners while scaling the product.

Lead development and maintenance of the banks Data and AI risk management framework, set data risk appetite and controls, translate regulatory expectations, codify standards, and ensure consistent definition and reporting of data risks and control gaps across the Group.

Lead and execute the regional data, analytics, and AI strategy while overseeing implementation, collaboration, team development, and governance compliance.