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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.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.