Data Intelligence Machine Learning Engineer

Dyson is seeking a Data Intelligence Machine Learning Engineer to join their team in Dubai, United Arab Emirates. This role is crucial for shaping Dyson's future through data, focusing on automating data labelling pipelines and reducing manual annotation.

Dyson is a global technology company driven by innovation in engineering, AI, and robotics. They are committed to solving real-world problems and creating better products through creativity and scientific advancement. Join a team that values exploration, discovery, and impact.

We are looking for a specialized engineer to design and implement in-house tools that automate our data labelling pipelines. Your primary goal will be to reduce our reliance on manual annotation by leveraging techniques like Active Learning, Weak Supervision, and Synthetic Data Generation. You will bridge the gap between raw data collection and model-ready datasets, ensuring high-quality labels at scale.

Company Overview

Dyson solves real-world problems and creates better products through the application of engineering, science, design and creativity. It is a family-owned, global technology company, founded by Sir James Dyson who remains at the helm alongside his son Jake. Dyson offers products across a growing range of areas: floorcare, air purification, robotics, haircare including formulations, lighting, hand drying, and most recently audio. Dyson continues to expand into new areas.

Dyson has global headquarters in Singapore and major technology campuses in Singapore, the UK, Malaysia, and the Philippines. Its global team of engineers, scientists and software developers are focused on developing technology-enabled products which work better and which people love to use. Key areas of focus have included high-speed electric digital motors, sensing and vision systems, robotics, machine learning and aerodynamics.

Quick Details

• Salary Range: Not Applicable
• Job Type: Full-time
• Qualifications: Bachelor’s or Master's degree in computer science, Engineering, Mathematics, Data Science, or a related field.
• Experience: At least 3+ years of professional experience in Machine Learning engineering, specifically focused on data centric-AI or computer vision/NLP pipelines.

Key Responsibilities

• Architect Labelling Pipelines: Design and deploy end-to-end automated labelling systems using frameworks like Snorkel, Cleanlab, or custom active learning loops.
• Develop "Human-in-the-Loop" (HITL) Systems: Build interfaces and workflows where models pre-label data and humans only intervene on high-uncertainty samples.
• Quality Assurance & Denoising: Implement algorithmic checks to identify and correct mislabelled or "noisy" data within existing datasets.
• Tooling & Integration: Collaborate with software engineers to integrate labelling tools with our existing data lakes and ML training infrastructure.
• Model Optimization: Fine-tune "teacher" models to generate high-quality pseudo-labels for "student" models.
• Set up and maintain robust data preparation infrastructure—optimising for data quality, speed, and seamless integration with downstream MLOps pipelines.

Requirements

• Proficiency in Python: Mastery of the Machine Learning stack (PyTorch or TensorFlow, NumPy, Pandas, Scikit-learn).
• Automated Labelling Expertise: Proven experience with Weak Supervision (labelling functions) or Active Learning strategies (uncertainty sampling, diversity sampling).
• Data Engineering: Experience with SQL and NoSQL databases, and managing large-scale unstructured data (images, text, or audio).
• Cloud Infrastructure: Familiarity with AWS (SageMaker Ground Truth), GCP (Vertex AI), or Azure ML labelling services.
• Version Control for Data: Experience with DVC (Data Version Control) or similar tools to track dataset iterations.
• Hands-on expertise building auto-labelling solutions or working with large-scale data annotation workflows.

Benefits

• Work alongside brilliant minds from Dyson global engineering team and external software/hardware partners in an environment built for exploration, discovery, delivery and impact.
• Opportunity to shape Dyson’s future through data and contribute to the next generation of connected devices.
• Engage in advanced data and feature engineering techniques to transform raw data into actionable insight.
• Collaborate closely with Data Scientists, Software Engineers, and Product teams to ensure high data quality and usability.
• Dyson is an equal opportunity employer, welcoming applications from all backgrounds.