At DSTA, we pioneer the development of Visual AI Solutions that address complex, mission-critical challenges and advance Singapore’s defence and security capabilities. Our team work at the intersection of applied research, engineering and operations, collaborating closely with experts across industry, academia, and Singapore’s Defence Technology Community. Join us to design, build, and deploy production-grade AI systems in an environment that values technical excellence, continuous learning and real-world impact.
Learn more about our work at our Medium Blog.
Opportunity
We are seeking a Visual AI Engineer to join a multidisciplinary team of engineers and researchers in Computer Vision, Multi-Modal AI and Generative AI for real-world defence and security applications. In this role, you will work across the full AI lifecycle, from problem formulation and model development to system integration and operational deployment. Your work will directly contribute to intelligent decision support, sensemaking, and mission-critical capabilities.
Key Responsibilities:
AI Model Design & Development:
- Design, develop and deploy scalable and efficient Computer Vision, Multi-Modal AI and Generative AI applications for information extraction, retrieval, sensemaking and decision-making.
- Perform pre-training, fine-tuning, and deployment of deep learning models, including detection and classification models, vision transformer models, visual data pipelines, and multi-modal models.
System Integration & Testing:
- Lead integration of AI modules and services into larger software systems
- Conduct comprehensive user testing and validation to ensure system robustness and operational readiness.
MLOps:
- Manage data pipelines and the AI model lifecycle, ensuring robustness, reliability and scalability of AI services.
Continuous Innovation:
- Stay at the forefront of developments in computer vision, generative AI, and multi-modal AI and drive continuous improvements.
- Propose and prototype new concepts to keep capabilities state-of-the-art.
Cloud AI Applications:
- Optimise AI models for on-premise private cloud environments, ensuring performance, robustness and system efficiency.
Edge AI Applications:
- Optimise AI models for low-latency, resource-constrained environments, ensuring real-time responsiveness and system efficiency.
Research and Development:
- Collaborate with research partners and explore emerging trends in areas such as multi-agent systems, knowledge representation and AI validation and verification.