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Build the AI that understands Southeast Asia
Applied Research
You work on machine learning and high-performance computing for real deployments.
Room to grow
Structured learning pathways, and mentorship from researchers who have taken systems into production.
Impact you can point to
Work that reaches Smart Nation initiatives and is deployed with consortium partners across Singapore.
Open roles
Product Manager
About the role
As Lead Product Manager, you're the connective tissue between business strategy, user experience, and engineering execution. We're looking for someone who enjoys pairing hard data and competitive research with the hands-on work of supporting an engineering team day to day. You'll define the roadmap, pressure-test it against real product analysis, and drive the cross-functional coordination needed to bring it to life.
What you'll do
- Shape the direction. Co-create the long-term vision, strategy, and roadmap for our AI-driven products, keeping them aligned with the wider organization's priorities.
- Bridge tech and experience. Turn complex model capabilities into clear PRDs and user stories that let engineering build with confidence.
- Understand our users. Lead user discovery and feedback loops, including research and usability testing, so we build things people actually need.
- Unify the teams. Act as translator between research scientists, MLOps engineers, developers, and partners so releases come out coherent, not stitched together.
- Manage the lifecycle. Guide products from proof-of-concept and MVP through to production-grade systems.
- Measure what matters. Define and track product metrics and adoption, using what you learn to keep iterating on the experience.
- Clear the roadblocks. Get in front of MLOps bottlenecks and data compliance questions before they slow the team down.
What we're looking for
- Education: Bachelor's degree in Computer Science, Information Technology, Engineering, Business with a technical focus, or a related field.
- Experience: 4+ years as a Product Manager, Technical Product Manager, or Product Owner, shipping software products, SaaS, or digital platforms.
- Product craft: Real experience managing backlogs, writing detailed PRDs, defining user personas, and working with roadmapping tools such as Jira Product Discovery, Productboard, or Confluence.
- Communication: Strong verbal and written skills, and a track record of influencing cross-functional teams without formal authority, translating technical AI concepts into business terms.
- Mindset: Comfortable in iterative environments where experimental AI models mean the product definition sometimes has to move fast.
AI Model Specialist
About the role
You'll lead the design, build, and scaling of our AI infrastructure, closing the gap between data science and software engineering with pipelines and practices that hold up in production. We're looking for someone who cares about building AI systems that are efficient, reusable, and pleasant for other engineers to work with.
What you'll do
- Work on multimodal AI. Design deep learning models that understand, align, and reason over audio and multimodal input.
- Connect research to reality. Translate current AI research into systems that hold up in production, with real systems-level thinking.
- Deploy at scale. Build low-latency deployment systems that stay robust and efficient under real load.
- Move fast with GenAI. Use generative AI tooling to speed up data engineering, model training, and prototyping.
- Own the full pipeline. Build end to end, from raw data processing and training through evaluation and deployment.
- Prototype and iterate. Work in a genuinely agile setup where new ideas get tested quickly and good ones move to production.
- Integrate across teams. Work with other disciplines to fold audio intelligence into larger enterprise AI systems.
What we're looking for
- Experience: 6+ years in Software Engineering, Platform Engineering, or DevOps, with at least 3+ years focused on MLOps, AI infrastructure, or ML engineering in a high-traffic production environment. Strong new graduates are also welcome to apply.
- Architecture: Experience designing and maintaining end-to-end ML platforms, not just deploying individual models.
- Engineering: Strong Python, plus at least one high-performance backend language (Go, C++, Rust, or Java). Code that's clean, tested, and production-ready.
- Cloud and containers: Comfortable with AWS, GCP, or Azure, and fluent in Docker, Kubernetes, and Helm.
Data Engineering Specialist
About the role
You'll lead the design, build, and scaling of our AI infrastructure, closing the gap between data science and software engineering with pipelines and practices that hold up in production. We're looking for someone who cares about building AI systems that are efficient, reusable, and pleasant for other engineers to work with.
What you'll do
- Lead on data. Provide technical leadership on data infrastructure, championing privacy-first architecture and risk-aware AI training.
- Build the engine. Design and optimize a modern data stack and ETL/ELT pipelines built for scale.
- Protect data integrity. Put validation frameworks in place so accuracy and consistency aren't left to chance.
- Align with strategy. Keep data infrastructure aligned with SDSO objectives and project needs, supporting analytics at enterprise scale.
- Bridge to the narrative. Make sure the technical constraints and assumptions behind our data work show up clearly in project proposals.
What we're looking for
- Experience: 6+ years in Software Engineering, Platform Engineering, or DevOps, with at least 3+ years focused on MLOps, AI infrastructure, or ML engineering in a high-traffic production environment. Strong new graduates are also welcome to apply.
- Architecture: Experience designing and maintaining end-to-end ML platforms, not just deploying individual models.
- Engineering: Strong Python, plus at least one high-performance backend language (Go, C++, Rust, or Java). Code that's clean, tested, and production-ready.
- Cloud and containers: Comfortable with AWS, GCP, or Azure, and fluent in Docker, Kubernetes, and Helm.
Lead MLOps Architect
About the role
You'll lead the design, build, and scaling of our AI infrastructure, closing the gap between data science and software engineering with pipelines and practices that hold up in production. We're looking for someone who cares about building AI systems that are efficient, reusable, and pleasant for other engineers to work with.
What you'll do
- Build the platform. Design the end-to-end MLOps infrastructure to train, deploy, and monitor models in production.
- Automate the lifecycle. Build CI/CD/CT pipelines for ML that get research into production without friction.
- Multiply the team's output. Build centralized feature stores, model registries, and reusable templates that speed up every data science pod.
- Optimize inference. Run high-throughput inference services, tuned for performance, scale, and cost.
- Keep systems reliable. Set up observability across ML systems, track drift and degradation, and put governance and testing in place.
- Lead and mentor. Act as the bridge between Data Science, Engineering, and Product, mentor junior engineers, and set the standard for MLOps across the org.
What we're looking for
- Experience: 6+ years in Software Engineering, Platform Engineering, or DevOps, with at least 3+ years focused on MLOps, AI infrastructure, or ML engineering in a high-traffic production environment. Strong new graduates with a deep interest in advanced AI capabilities are welcome to apply.
- Architecture: Experience designing and maintaining end-to-end ML platforms, not just deploying individual models.
- Engineering: Strong Python, plus at least one high-performance backend language (Go, C++, Rust, or Java). Code that's clean, tested, and production-ready.
- Cloud and containers: Comfortable with AWS, GCP, or Azure, and fluent in Docker, Kubernetes, and Helm.
- Serving: Hands-on with low-latency inference frameworks such as Triton, Ray Serve, BentoML, TF Serving, or TorchServe.
- Pipelines: Experience with CI/CD/CT and orchestration tools like Kubeflow, Airflow, MLflow, ArgoCD, or Metaflow.
- Reusability: Track record with feature stores (Feast, Hopsworks) and model registries.
- Observability: Experience monitoring for data drift and performance degradation with tools like Prometheus, Grafana, Arize, Evidently, or Fiddler.
- Infrastructure as code: Solid with Terraform, Ansible, or CloudFormation.
Senior AI Engineer
About the role
You'll lead the design, build, and scaling of our AI infrastructure, closing the gap between data science and software engineering with pipelines and practices that hold up in production. We're looking for someone who cares about building AI systems that are efficient, reusable, and pleasant for other engineers to work with.
What you'll do
- Architect the workflows. Turn high-level business goals into robust, autonomous multi-step agentic workflows that deliver real value.
- Own the stack. Drive core agentic components, define the tech stack, and integrate the right tools for each use case.
- Design for collaboration. Build multi-agent communication frameworks, memory management, context pipelines, and human-in-the-loop feedback so behavior stays predictable.
- Work on AI safety. Partner with the AI Safety team on secure, private systems, and help mitigate semantic drift and track confidence over long-horizon tasks.
- Set the bar. Build evaluation and benchmarking protocols to measure and improve autonomous systems honestly.
What we're looking for
- Experience: 6+ years in Software Engineering, Platform Engineering, or DevOps, with at least 3+ years focused on MLOps, AI infrastructure, or ML engineering in a high-traffic production environment.
- Architecture: Experience designing and maintaining end-to-end ML platforms, not just deploying individual models.
- Engineering: Strong Python, plus at least one high-performance backend language (Go, C++, Rust, or Java). Code that's clean, tested, and production-ready.
- Cloud and containers: Comfortable with AWS, GCP, or Azure, and fluent in Docker, Kubernetes, and Helm.
AI Engineer
About the role
You'll help design, build, and scale our AI infrastructure, closing the gap between data science and software engineering with pipelines and practices that hold up in production. We're looking for someone who cares about building AI systems that are efficient, reusable, and pleasant for other engineers to work with.
What you'll do
- Architect the workflows. Turn high-level business goals into robust, autonomous multi-step agentic workflows that deliver real value.
- Own the stack. Drive core agentic components, define the tech stack, and integrate the right tools for each use case.
- Design for collaboration. Build multi-agent communication frameworks, memory management, context pipelines, and human-in-the-loop feedback so behavior stays predictable.
- Work on AI safety. Partner with the AI Safety team on secure, private systems, and help mitigate semantic drift and track confidence over long-horizon tasks.
- Set the bar. Build evaluation and benchmarking protocols to measure and improve autonomous systems honestly.
What we're looking for
- Experience: 6+ years in Software Engineering, Platform Engineering, or DevOps, with at least 3+ years focused on MLOps, AI infrastructure, or ML engineering in a high-traffic production environment. Strong new graduates are also welcome to apply.
- Architecture: Experience designing and maintaining end-to-end ML platforms, not just deploying individual models.
- Engineering: Strong Python, plus at least one high-performance backend language (Go, C++, Rust, or Java). Code that's clean, tested, and production-ready.
- Cloud and containers: Comfortable with AWS, GCP, or Azure, and fluent in Docker, Kubernetes, and Helm.