At Dyson, we’re driven by a relentless pursuit of innovation across engineering, AI, and robotics. Our new Data Intelligence team is looking for a specialized Machine Learning Engineer to design and implement in-house tools that automate our data labelling pipelines, reducing reliance on manual annotation through Active Learning, Weak Supervision, and Synthetic Data Generation.
About the Role – Data Intelligence at Dyson
Core Mission: Automate data labelling pipelines to reduce manual annotation reliance
Key Techniques: Active Learning, Weak Supervision, and Synthetic Data Generation
Frameworks: Snorkel, Cleanlab, or custom active learning loops
Cloud & Tools: AWS SageMaker Ground Truth, GCP Vertex AI, Azure ML labelling services, DVC
Team Environment: Collaboration with Dyson’s global engineering team and external software/hardware partners
Career Growth & Cutting-Edge AI Exposure
Strategic Location: Dubai – a growing hub for Dyson’s global engineering and AI operations
Frontier Data-Centric AI: Work directly on techniques shaping the next generation of connected devices
Cross-Functional Collaboration: Partner with Data Scientists, Software Engineers, and Product teams
Career Growth: Build specialist expertise in automated labelling and data-centric AI at a global engineering brand
Position Overview
This Data Intelligence Machine Learning Engineer role involves architecting end-to-end automated labelling pipelines using frameworks like Snorkel and Cleanlab, building Human-in-the-Loop (HITL) systems where models pre-label data and humans intervene only on high-uncertainty samples, implementing quality assurance and denoising checks, collaborating with software engineers to integrate labelling tools with data lakes and ML infrastructure, fine-tuning teacher models for pseudo-labeling, and performing data visualization and feature engineering to support both research and deployment.
Why This Role Matters: As Data Intelligence Machine Learning Engineer at Dyson, you directly reduce manual annotation costs by building automated, intelligent labelling pipelines, apply cutting-edge techniques like Active Learning and Weak Supervision to real production ML systems, bridge the gap between raw data collection and model-ready datasets at scale, collaborate with Dyson’s global engineering team on next-generation connected devices, and build specialist expertise in one of the fastest-growing niches within data-centric AI.
Key Responsibilities
Automated Labelling Pipeline Architecture
- Design and deploy end-to-end automated labelling systems using Snorkel, Cleanlab, or custom active learning loops
- Develop Human-in-the-Loop (HITL) systems where models pre-label data and humans intervene on high-uncertainty samples
Quality Assurance & Data Denoising
- Implement algorithmic checks to identify and correct mislabelled or noisy data within existing datasets
- Perform data visualization and in-depth analysis using advanced data and feature engineering techniques
Tooling & Model Optimization
- Collaborate with software engineers to integrate labelling tools with data lakes and ML training infrastructure
- Fine-tune teacher models to generate high-quality pseudo-labels for student models
Data Infrastructure & Cross-Team Collaboration
- Set up and maintain robust data preparation infrastructure optimized for quality, speed, and MLOps integration
- Work closely with Data Scientists, Software Engineers, and Product teams to ensure high data quality and usability
Qualifications & Requirements
Educational Requirements
- Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Data Science, or a related field
Experience Requirements
- At least 3+ years of professional experience in Machine Learning engineering, focused on data-centric AI or computer vision/NLP pipelines
- Proven experience with Weak Supervision or Active Learning strategies (uncertainty sampling, diversity sampling)
Technical Skills
- Proficiency in Python and mastery of the ML stack (PyTorch or TensorFlow, NumPy, Pandas, Scikit-learn)
- Experience with SQL and NoSQL databases, managing large-scale unstructured data (images, text, audio)
- Familiarity with AWS SageMaker Ground Truth, GCP Vertex AI, or Azure ML labelling services
- Experience with DVC (Data Version Control) or similar tools to track dataset iterations
Additional Skills
- Experience designing, deploying, and maintaining scalable data pipelines including cleansing, transformation, and storage
- Strong background in feature engineering, data analysis, and visualization (Jupyter, Tableau, or Power BI)
- Great communicator who documents solutions clearly and collaborates across technical and non-technical teams
About Dyson’s Data Intelligence Team
Dyson’s Data Intelligence team sits at the heart of the company’s mission to shape the future through data, blending creativity, precision, and audacity to power intelligent products. Working alongside Dyson’s global engineering team and external software/hardware partners, this team crafts the data strategies and pipelines that fuel the next generation of connected devices, in an environment built for exploration, discovery, delivery, and impact.
Career Excellence: Join Dyson’s Data Intelligence team and build the automated labelling infrastructure powering the next generation of connected products.
Who Should Apply?
- Data-Centric AI Engineers: With 3+ years focused on automated labelling and dataset quality
- Active Learning/Weak Supervision Specialists: Experienced with Snorkel, Cleanlab, or similar frameworks
- Computer Vision/NLP ML Engineers: Comfortable managing large-scale unstructured data
- MLOps-Minded Engineers: Skilled in cloud labelling services and DVC-based data versioning
- Cross-Functional Collaborators: Excited to work with Data Scientists, Engineers, and Product teams at Dyson
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