Top Machine Learning Jobs in Singapore

Customer-facing solutions architect specializing in OpenShift and AI workloads. Lead technical workshops, POCs, architecture walkthroughs, and demos; advise customers on AI infrastructure (compute, networking, storage, GPUs) and help transition AI/ML initiatives into production using OpenShift and related platform capabilities.

Pre-sales OpenShift specialist focused on AI who leads architecture walkthroughs, custom demos, workshops and POCs, advises on AI/ML platform readiness, partners with sales/product teams, and helps customers deploy AI workloads to production on OpenShift.

Pre-sales OpenShift Solutions Architect focused on AI workloads. Lead technical strategy, demos, workshops, and POCs; advise customers on AI/ML infrastructure, deployment, and optimization; collaborate with product, sales, and partners to influence roadmaps and deliver reusable technical sales assets and best practices.

Lead A1's research and intelligence direction, defining architectures, evaluation frameworks, alignment and safety, and guiding exploration of frontier techniques (retrieval-augmented training, MoE, distillation, multi-agent orchestration, multimodal). Partner closely with product and engineering to deliver reliable, long-running, context-rich AI features and set technical standards across the organization.

Own end-to-end ML systems: data, training, evaluation, inference, and deployment for production-grade LLMs. Build scalable training and inference pipelines, optimize GPU performance and memory, maintain evaluation and safety tooling, and collaborate with application engineers to integrate models into products while balancing latency, cost, and reliability.

Lead execution of end-to-end ML systems: data pipelines, training workflows, evaluation, and inference. Translate research into production by fine-tuning large models (LoRA/QLoRA/SFT/DPO/distillation), designing scalable GPU inference, ensuring memory/latency optimization, building evaluation for performance/robustness/safety, and integrating ML into backend and client products while operating under real-world constraints.

Lead production-grade ML systems: build and operate end-to-end ML pipelines, fine-tune large models (LoRA/QLoRA/SFT/DPO/distillation), design scalable inference, ensure GPU optimization, implement evaluation for performance/robustness/safety, and collaborate with product and application teams to ship reliable, low-latency AI features.

Design, build, and deploy core ML infrastructure components on Kubernetes. Develop scalable services for data ingestion, processing, training, and evaluation. Write maintainable code for high-throughput, petabyte-scale systems. Own features end-to-end from design through production and operation to accelerate autonomous vehicle model development.

The Staff Machine Learning Engineer at Coupang Play will develop recommendation and personalization systems, manage data collection, and validate model performance.

Design and build a scalable, reliable ML research platform supporting large-scale experimentation, distributed GPU training, experiment tracking, model/version/ artifact management, data pipelines, observability, and resource scheduling across HPC and multi-cloud environments while partnering with quantitative researchers.

Designs and maintains CI/CD pipelines, model deployment and rollback workflows, monitoring and observability for ML in production, and manages model lifecycle with MLflow while automating infrastructure and partnering with ML and platform teams.

Lead QA for AI-powered SaaS features: design and execute functional, API, integration, regression, exploratory, and end-to-end tests. Validate AI outputs, investigate root causes across frontend/backend/APIs/databases, build automation, improve CI/CD quality gates, and collaborate with engineers, product, and security teams to raise release confidence.

Build and operate ML infrastructure and platforms for model training, evaluation, deployment, and inference. Improve reliability, scalability, latency, throughput, cost efficiency, observability, and tooling to enable rapid experimentation and production-ready AI systems.

The Senior Machine Learning Engineer will design, train, and deploy ML models for optimizing revenue in advertising, taking ownership of product development and leading the ML discipline.

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.

The ML Systems Engineer will optimize distributed training systems, develop high-performance GPU kernels, and ensure efficient data pipelines for machine learning models.

Train and research transformer-based embedding models for web-scale search: design novel architectures, create large-scale datasets and evaluations, implement models and training pipelines (pretraining and fine-tuning), and improve internal state-of-the-art retrieval performance.

Lead and govern a program composed of multiple projects: plan, align scope with business priorities, manage risks and interdependencies, coordinate stakeholders, report status to senior management, drive cross-team collaboration, and optimize resourcing and budgeting.

Design, implement, and optimize perception algorithms (detection, tracking, segmentation) using camera, lidar, and radar. Build sensor fusion, productionize models for embedded real-time systems, deploy to vehicle fleets, analyze large datasets, collaborate cross-functionally, and mentor junior engineers.

Design, operate, and optimize distributed training and serving infrastructure for large physics models on GPU/HPC platforms. Solve large-scale data I/O bottlenecks, build experiment tracking and observability, enable reproducible model packaging and deployment, and improve research developer experience while collaborating closely with research scientists and ML engineers.

Lead design and implementation of Red Hat OpenShift AI platform solutions for enterprise customers across ASEAN. Deploy and manage AI/ML workloads, architect generative and agentic AI solutions, run discovery and design workshops, support pre-sales and proofs-of-concept, define platform best practices, mentor junior consultants, and contribute to internal playbooks and open-source AI initiatives.

The Research Associate will develop models for lithium-ion batteries, design algorithms for state estimation, and improve wireless charging optimization. Responsibilities include data analysis, simulation modeling, experimental validation, and collaboration on research projects.

Design and integrate Visual-Language Models into robotic platforms, enabling semantic reasoning and decision-making for autonomous robots. Responsibilities include simulation, data handling, and collaboration on research projects.

Design, build, deploy and operate an internal AI platform and agentic applications: retrieval/RAG, deployment CI/CD, monitoring, security-aware data access, and end-to-end ML microservice lifecycle. Provide architecture leadership, mentor engineers, and partner with InfoSec, product and data teams.

Design and deliver production-grade, enterprise-focused AI architecture blueprints and open-source Quickstarts. Collaborate with partners to integrate and benchmark systems, incorporate telemetry, ensure secure and compliant deployments, and optimize runtimes across heterogeneous hardware.