Machine Learning Engineer (Video Analytics)

About the Role

We are looking for a talented and motivated Machine Learning Engineer to join the Group Engineering Centre (GEC) – Video Analytics team at ST Engineering. You will work as part of a high‑performing engineering team, contributing to the delivery of advanced video analytics and AI solutions that support multiple businesses across the Group.

This role is well suited to engineers who enjoy solving challenging real‑world problems and want hands‑on exposure to computer vision, machine learning, and foundation models (LLMs / VLMs) in production environments. You will collaborate closely with senior engineers, product stakeholders, and domain experts, gaining experience across a wide range of industries and use cases.

Key Responsibilities

Machine Learning & Computer Vision Development

• Design, develop, train, and evaluate machine learning and computer vision models for video analytics applications, including model optimisation techniques such as pruning, quantisation, and knowledge distillation for edge deployment.

• Implement solutions for tasks such as object detection, tracking, classification, and video understanding.
• Apply deep learning techniques under the guidance of senior engineers to solve complex, real‑world problems.
• Support experimentation and benchmarking of models, features, and algorithms.

Application of LLMs / VLMs

• Contribute to projects that utilise Large Language Models (LLMs) and Vision‑Language Models (VLMs) for innovative video analytics solutions.
• Assist in integrating foundation models into end‑to‑end pipelines for tasks such as video reasoning, summarisation, and event analysis.
• Continuously learn and apply emerging techniques in multimodal and generative AI.

Software Engineering & Deployment

• Write clean, maintainable, and well‑tested code following team standards and best practices.
• Support the deployment of ML models into production systems, including optimisation for performance and reliability.
• Collaborate with teammates on debugging, performance tuning, and iterative improvements.
• Contribute to shared libraries, tools, and reusable components across the team.

Collaboration & Learning

• Work with other engineers, learning from their experiences and technical discussions.
• Collaborate with cross‑functional teams, including system engineers and business stakeholders.
• Actively participate in knowledge sharing, technical discussions, and continuous improvement activities within GEC.

Required Skills & Experience

Essential

• Experience developing machine learning or computer vision solutions, either in industry or advanced academic projects.
• Strong foundations in machine learning and deep learning, including model training and evaluation.
• Practical experience with frameworks such as PyTorch or TensorFlow.
• Proficiency in Python and good software engineering practices.
• Understanding of common computer vision techniques and architectures.
• A proactive mindset with a strong willingness to learn and grow technically.

Desirable

• Exposure to video analytics or sequence‑based modelling.
• Familiarity with LLMs and/or VLMs and their use in applied AI solutions.
• Experience with data pipelines, model optimisation, or deployment workflows.
• Knowledge of MLOps concepts, containers, or cloud‑based ML platforms.
• Bachelor’s or higher degree in Computer Science, Engineering, AI, or a related discipline.

What We Offer

• The opportunity to work on impactful, real‑world AI projects deployed across multiple industries.
• Close mentorship and technical guidance from senior and principal engineers.
• Exposure to cutting‑edge video analytics, computer vision, and foundation models.
• A collaborative, supportive environment within a group‑level engineering centre.
• Competitive remuneration and benefits, with strong emphasis on learning and career development.

If you are eager to develop your expertise in machine learning and video analytics, and want to be part of a team delivering AI solutions at enterprise scale, we would be pleased to hear from you.