Machine Learning Engineer (DSC|SN)
Headquartered in Singapore, ST Engineering ranks among the largest companies listed on the Singapore Exchange.
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The incumbent will design, build, and deploy agentic AI models, ensuring robust performance across digital twin simulations and real-world production systems.
This role calls for a hands-on Machine Learning Engineer with a deep understanding of both intelligent agent behavior and ML infrastructure, capable of bridging experimental models with production-grade AI solutions.
Responsibilities- Design and implement agentic AI models capable of autonomous decision-making, planning, and tool use in complex environments.
- Develop and train machine learning models tailored for both simulation-based (digital twin) testing and live production deployment.
- Translate high-level behavioral specifications into scalable, modular ML architectures that enable adaptive and interactive agents.
- Collaborate with data scientists, AI architects, and systems engineers to integrate models into broader AI pipelines and system architectures.
- Optimize models for efficiency, robustness, and real-time performance within distributed and resource-constrained environments.
- Implement continuous training, fine-tuning, and model evaluation workflows to support evolving agent behavior and dynamic environments.
- Ensure alignment of model outputs with system goals, safety constraints, and operational performance metrics.
- Contribute to MLOps pipelines, including model versioning, deployment automation, monitoring, and rollback mechanisms.
Requirements
Experience- 5+ years of experience in machine learning engineering or applied AI, including model deployment in production environments.
- Demonstrated success in developing and deploying ML models within simulation and real-time operational contexts.
- Proficiency in ML frameworks such as PyTorch, TensorFlow, and tools for agentic and reinforcement learning (e.g., Ray RLlib, Stable-Baselines).
- Strong experience with model training, evaluation, and deployment across both digital twins and live systems.
- Familiarity with MLOps, CI/CD pipelines for ML, and monitoring tools for model performance and drift detection.
- Understanding of agent-based modeling, planning algorithms, and state/action representations.
- Experience with containerization (Docker), orchestration (Kubernetes), and cloud platforms (AWS, GCP, or Azure) for ML workloads.
- Exposure to agentic AI systems, multi-agent coordination, or intelligent automation frameworks.
- Familiarity with digital twin architectures, synthetic data generation, and simulation-to-reality transfer techniques.
- Experience integrating models into service-oriented or microservice architectures.
Work location: Jurong East
Find out more: https://www.stengg.com/cybersecurity
ST Engineering believes in fostering a culture where team members are encouraged to overcome challenges, explore new ideas, and work together to succeed. We value individuals who are determined to push beyond the boundaries, and have a thirst for knowledge, continuous learning, and self-improvement.