Principal Machine Learning Engineer (Video Analytics)

Principal Machine Learning Engineer (Video Analytics) Location Group Engineering Centre, ST Engineering Jurong East Hub (Singapore) About the Role We are seeking an exceptional Assistant/ Principal Machine Learning Engineer to lead the delivery of high‑impact, production‑grade AI solutions across multiple business domains within ST Engineering . Based within the Group Engineering Centre (GEC) – Video Analytics team , this role plays a critical part in enabling and accelerating advanced video analytics capabilities across all ST Engineering businesses. You will operate at both a hands‑on technical and technical leadership level, tackling complex real‑world problems by applying Computer Vision, Machine Learning, and Large / Vision‑Language Models (LLMs / VLMs) in innovative ways. In addition to technical delivery, you will help shape technical direction , mentor engineers , and influence best practices across projects and teams. This role is ideal for a seasoned engineer who enjoys working on challenging, cross‑domain problems , thrives in a collaborative environment, and is motivated by seeing AI solutions deployed at scale. Key Responsibilities Technical Leadership & Delivery Lead the design, development, and deployment of advanced video analytics and AI systems across diverse domains such as smart cities, transport, defence, security, and industrial solutions. Architect reusable, scalable, robust, and maintainable machine learning and computer vision solutions suitable for enterprise and mission‑critical environments. Drive the adoption of state‑of‑the‑art ML, CV, and foundation models , including LLMs and VLMs, to solve complex video understanding problems. Translate ambiguous business and operational requirements into clear technical strategies and implementable solutions . Provide technical oversight across multiple projects, ensuring engineering quality, performance, and reliability. Innovation & Applied Research Explore and apply emerging research in computer vision, multimodal learning, self‑supervised learning, and generative AI to real‑world video analytics use cases. Prototype and evaluate novel approaches involving LLMs/VLMs for tasks such as video reasoning, summarisation, event understanding, and human‑AI interaction. Balance innovation with pragmatism, ensuring solutions are deployable, supportable, and cost‑effective . Team Leadership & Mentorship Act as a technical mentor and role model to machine learning and software engineers within the Video Analytics team. Provide guidance on system design, model development, experimentation, and MLOps best practices. Support the growth of engineering capability across GEC by sharing knowledge and establishing reusable frameworks, tools, and patterns. Contribute to technical reviews, hiring activities, and capability planning as a senior member of the engineering community. Stakeholder Collaboration Work closely with business units, product owners, domain experts, and system integrators across ST Engineering. Clearly communicate technical concepts, trade‑offs, and risks to both technical and non‑technical stakeholders. Support pre‑sales, feasibility studies, and solution consultations where deep technical expertise is required. Required Skills & Experience Essential Extensive experience (typically 4+ years) in Machine Learning and/or Computer Vision , with a strong track record of delivering solutions to production. Deep expertise in computer vision techniques (e.g. detection, tracking, segmentation, action recognition, video understanding). Strong practical experience with deep learning frameworks such as PyTorch or TensorFlow. Proven experience designing and deploying end‑to‑end ML systems , including training, evaluation, optimisation, and deployment. Hands‑on experience leveraging LLMs and/or Vision‑Language Models as part of applied AI solutions. Strong software engineering fundamentals, including Python, system design, and version control. Experience working in complex, multi‑stakeholder or multi‑domain environments. Desirable Experience with MLOps , model lifecycle management, and production monitoring. Familiarity with edge AI or resource‑constrained deployment scenarios. Exposure to cloud platforms, containerisation, or distributed systems. Experience leading or mentoring engineers in a technical capacity. Ability to apply audio analysis and audio‑visual learning techniques, such as sound event detection, speech‑related analysis, or audio‑visual fusio…