Lead Data Intelligence Machine Learning Engineer

Dyson; Dubai; On-site; AED 420,000 - 700,000; Full time

Job summary

Dyson seeks a Lead Data Intelligence ML Engineer to design automated data-labelling pipelines, reducing manual annotation via Active Learning and Weak Supervision. You will bridge data collection and model-ready datasets, ensuring high-quality labels at scale.

You will collaborate with Dyson’s global engineering teams and external partners, shaping data strategies, and building robust data prep and MLOps infrastructure in a fast-moving environment.

Qualifications

• 8+ years in ML engineering focused on data-centric AI or CV/NLP pipelines.
• Strong Python, ML stack expertise (PyTorch/TensorFlow, NumPy, Pandas, scikit-learn).
• Experience with automated labelling, weak supervision, or active learning.
• Data engineering with large-scale unstructured data; SQL/NoSQL.
• Cloud ML environments (AWS, GCP, Azure) and ML pipelines.

Responsibilities

• Architect end-to-end automated labelling pipelines using Snorkel/ Cleanlab or active learning loops.
• Build HITL systems with interfaces for high-uncertainty samples.
• Implement QA and denoising to fix mislabeled data.
• Integrate labelling tools with data lakes and ML training infra.
• Fine-tune teacher models to generate high-quality pseudo-labels.
• Maintain data prep infra for data quality and speed.
• Perform data visualization and in-depth feature analysis to drive insights.
• Collaborate with Data Scientists, Software Engineers, and Product teams.

Skills

Python

PyTorch

TensorFlow

NumPy

Pandas

Scikit-learn

Active Learning

Weak Supervision

Data Visualization

Jupyter

Data Pipelines

Cloud ML (SageMaker, Vertex AI, Azure)

SQL/NoSQL

DVC

Labelling Tools

Education

Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Data Science

Tools

Snorkel

Cleanlab

AWS SageMaker Ground Truth

Vertex AI

Azure ML

DVC

Tableau/Power BI

Jupyter

Job description

Job Description:

About Us

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.

About The Role

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.

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.


• 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.

About you

• 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 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.

Requirements: