At Dyson were driven by a relentless pursuit of innovationpushing boundaries in engineering AI and robotics. Our new Data Intelligence team sits at the heart of this mission: shaping Dysons 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.
Youll work alongside brilliant minds from Dyson global engineering team and external software/hardware partners in an environment built for exploration discovery delivery and impact.
We are looking for a specialized Lead Data Intelligence Machine Learning 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.
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 infrastructureoptimising 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. Youll 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 8 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 visualizationcomfortable 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.
Bachelors or Masters degree in computer science Engineering Mathematics Data Science or a related field.
Dyson is an equal opportunity employer. We know that great minds dont 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.
Required Experience:
IC
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