At Dyson, we’re driven by a relentless pursuit of innovation—pushing boundaries in engineering, AI, and robotics. Our new Data Intelligence team sits at the heart of this mission: shaping Dyson’s future through data. Here, we blend creativity, precision, and audacity to power intelligent products. We craft data strategies and pipelines that fuel the next generation of connected devices.
You’ll work alongside brilliant minds from Dyson global engineering team and external software/hardware partners in an environment built for exploration, discovery, delivery and impact.
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.
Perform data visualization and in-depth analysis using advanced data and feature engineering techniques. You’ll help transform raw data into actionable insight, supporting both research and deployment.
Work closely with Data Scientists, Software Engineers, and Product teams to ensure high data quality and usability across products and projects.
At least 3+ years of professional experience in Machine Learning engineering, specifically focused on data centric-AI or computer vision/NLP pipelines.
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.
Advanced skills in Python (and/or other relevant languages), and experience with key ML/data science libraries (e.g. TensorFlow, PyTorch, scikit-learn, pandas).
Experience designing, deploying, and maintaining scalable data pipelines, including data cleansing, transformation, and storage (cloud, on-prem, or hybrid).
Strong background in feature engineering, data analysis, and data visualization—comfortable using tools like Jupyter, Tableau, or Power BI.
Great communicator who documents solutions clearly and collaborates effortlessly across technical and non-technical teams.
Able to balance speed and quality, stay curious about new developments, and deliver results in a fast-moving environment.
Bachelor’s or Master's degree in computer science, Engineering, Mathematics, Data Science, or a related field.
Dyson is an equal opportunity employer. We know that great minds don’t think alike, and it takes all kinds of minds to make our technology so unique. We welcome applications from all backgrounds and employment decisions are made without regard to race, colour, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other any other dimension of diversity.
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.
Since inventing the first cyclonic bagless vacuum cleaner, the DC01, Dyson has consistently invested in research and development to improve its products and technologies radically. 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.
Today, Dyson sells products in more than 80 markets, has 450 Dyson stores worldwide and is available in all major technology and beauty retailers. 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.
Beyond products, to encourage an inventive future, Dyson is also inspiring the next generation of engineers and inventors through the Dyson Institute of Engineering and Technology, the James Dyson Foundation and the James Dyson Award.
The Dyson family applies its problem-solving approach in other fields, and established Dyson Farming in 2012. It is one of the largest farming businesses in the UK, extending to 36,000 acres across Lincolnshire, Oxfordshire, Gloucestershire and Somerset. It is a family-owned enterprise unlike any other, focussed on long-term investment in British farming and the countryside to grow tasty and nutritious food.
