Top Machine Learning Jobs in Singapore

The role involves driving technical sales strategy for AI solutions, consulting on data architectures, and presenting to C-level executives, requiring extensive customer-facing experience and AI proficiency.

Lead global analytics engineering initiatives for electronic trading and research. Design, build, and optimize real-time data pipelines, scalable APIs, analytics libraries, and production trading tools. Apply AI to data engineering, analytics, and SDLC automation while ensuring reliability, testing, security, and continuous delivery. Collaborate with trading and research teams, onboard datasets, mentor engineers, manage workstreams, and establish technical standards across distributed teams.

Lead and grow a team of CMP and BTG engineers to develop and transfer CMP/BTG process modules for 3D NAND. Drive process characterization, DOE-based optimization, process margin and manufacturability improvements, supplier partnerships, pilot ramp/yield support, and adoption of data analytics/AI for predictive modeling and process control.

Leads smart manufacturing data science initiatives for the F10 organization, serving as the bridge between site leadership, users, and enterprise teams. Responsibilities include prioritizing requirements, driving solution adoption, balancing local support with network best-practice deployment, evaluating capabilities, coordinating cross-functional projects, monitoring deliverables, and managing competing resources and priorities.

Conduct ML/DL research to develop predictive models from large market datasets, integrate models into systematic trading strategies, contribute to AI/ML libraries, and deploy production algo systems with P&L responsibility across FX, Rates, Commodities, and Credit.

Engineers at Citadel work in small teams to develop high-performing technology under quick cycles. They focus on creating tools for trading strategies and data research platforms.

Summer intern role building high-performance, large-data research platforms and web frameworks. Collaborate in small teams to create tools for trading strategies, applying distributed computing, NLP, and machine learning.

This internship involves assisting in machine learning development, AI automation, data engineering, and exploring innovative projects to enhance test efficiency and engineering productivity.

Lead advanced data analytics and ML initiatives to improve yield, quality, and reliability for HBM products. Develop ML/DL models, build data infrastructure, integrate solutions into disposition workflows, mentor team members, and collaborate cross-functionally to optimize KPIs including quality, cost, cycle time, and scale.

Analyze card fraud disputes, trends, datasets, and prevention-rule performance to identify fraud patterns and reduce losses. Develop, test, optimize, and monitor fraud rules using data and machine-learning risk signals while balancing customer experience, approval rates, and costs. Investigate incidents and emerging attacks, support dashboards and KPIs, collaborate with Product, Engineering, Data Science, and Operations teams, and communicate recommendations to technical and non-technical stakeholders.

This role involves leading NAND technology integration, solving technology gaps, designing experiments, and driving technical innovation in memory solutions.

Lead a ~10-person analytics team for APAC, partnering with Product, Banking and Engineering to drive data-led product decisions, define KPIs, build measurement frameworks, coach analysts, and contribute to regional analytics strategy and planning.

Design and deploy advanced process control, automation, and analytics solutions for semiconductor manufacturing. Collaborate with process teams to apply statistical analysis and machine learning, maintain global control applications, develop training, and integrate AI-assisted tools to improve yield, quality, and efficiency.

As a Customer Success Engineer, you will manage enterprise customer relationships, provide technical expertise, and drive integration and adoption of Deepgram's voice AI solutions, ensuring customer success and identifying growth opportunities.

Lead development of advanced dry etch processes for NAND technology, inventing plasma chemistries and equipment capabilities. Use AI/ML, analytics, and automation to accelerate process pathfinding, collaborate with integration and device teams, and drive manufacturability and yield improvements across semiconductor process domains.

Develop and deploy AI/ML and analytics solutions for Facilities Mechanical systems (HVAC, chillers, boilers, CDA, cleanroom air). Build predictive maintenance models, dashboards, digital twins, and automation to improve reliability, energy efficiency, and operational decision-making. Collaborate with controls, IT, and operations to integrate SCADA/BMS/PLC data, ensure data quality and governance, and drive digitalization of maintenance workflows while supporting change management in a 24/7 fab environment.

Operate and maintain SEMs and related sample-prep tools for NAND development, perform construction analysis, troubleshoot and optimize tools, support engineers, and help develop automated/AI-assisted lab workflows and reporting.

Lead and grow a team of diffusion and implant engineers to develop and transfer 3D NAND process modules. Drive process characterization, DOE-driven optimization, process monitoring/control, manufacturability, supplier partnerships, and adoption of data analytics/AI to improve yield, cost, and scalability while supporting pilot ramps and high-volume manufacturing.

Lead Fab10 electrical and control engineering for safe, reliable semiconductor facility operations. Manage team performance, HVAC/PDU/HV-MV-LV power systems, PLC/SCADA, predictive maintenance, asset management, and obsolescence strategies. Support capacity expansions, tool installs, contractor oversight, energy efficiency, and adoption of AI/ML and digital technologies to improve reliability and operational excellence.

Lead development and operation of automated FIB/TEM workflows for NAND technology: run daily TEM/FIB tasks, create automation templates and scripts, apply ML to optimize recipes and data, transfer solutions across teams, collaborate with vendors, ensure data quality and safety, and mentor engineers.

Lead development, qualification, and manufacturing transfer of advanced packaging (hybrid bonding, TCB, flip chip). Design and analyze DOEs, perform process characterization, root-cause analysis, and yield/reliability improvement. Collaborate cross-functionally, evaluate materials/equipment, apply AI/automation and analytics, and support sample builds and supplier engagements toward high-volume readiness.

Develop and deploy product-disposition workflows and deviation alert systems using statistical tools and ML/AI to improve yield, reliability, and quality. Build algorithms for data mining, pattern recognition, and wafer/die screening. Collaborate with Fab, design, and quality teams, mentor team members, and drive technical innovation to meet KPIs like quality, cost, cycle time, and scale.

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.

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.

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.