Job at a Glance
- 📍 Location: Dubai, Dubai, United Arab Emirates
- 🏢 Company: Dyson
- 💼 Employment Type: Full-time
- 📊 Seniority: Not Applicable
- 🗂️ Function: Engineering and Information Technology
- 🏭 Industry: Appliances, Electrical, and Electronics Manufacturing
- 📅 Date Posted: 2026-08-08
- 🌐 Category: Artificial Intelligence
About the Role & Company
A new Data Intelligence Machine Learning Engineer opening at Dyson in Dubai, Dubai, United Arab Emirates just went live. You’d be plugging into a Artificial Intelligence team where the workload is growing, not shrinking. In practice, that means faster feedback loops, more visible ownership, and less waiting your turn. Core Data Intelligence Machine Learning Engineer deliverables sit at the center, with cross-functional work layered on when priorities shift. GCC demand for artificial intelligence skills is still healthy, and Dubai continues to sit at the top of the regional pay bands. It’s a solid entry point for candidates who want regional exposure without giving up ownership of real work — you’re contributing, not observing.
Key Requirements
- Real-world at least 3+ years of professional experience in Machine Learning engineering, specifically focused on data centric
- Automated Labelling Hands-on experience: 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
- 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
- on hands-on experience building auto
- Advanced skills in Python (and/or other relevant languages), and experience with key ML/data science libraries (e
- Experience designing, deploying, and maintaining scalable data pipelines, including data cleansing, transformation, and storage (cloud, on
Job Description
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.
we’re looking for a specialized Data Intelligence Machine Learning Engineer to design and put in place 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’ll 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.
Build “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: Put in place algorithmic checks to identify and correct mislabelled or “noisy” data within existing datasets.
ATS Resume Tips for This Role
To get past Applicant Tracking Systems (ATS) for this Data Intelligence Machine Learning Engineer opening, tailor your resume with the pointers below:
- Include these keywords verbatim in your Skills and Experience sections: Python, TensorFlow, PyTorch, Machine Learning, NLP, Computer Vision, MLOps, Data Pipelines, SQL, Deep Learning. ATS parsers match exact strings, not synonyms.
- Use a single-column layout. Two-column resumes break parsers — text gets scrambled or dropped.
- Save as .docx or standard .pdf (not scanned/image PDF). Avoid text boxes, headers, footers, graphics.
- Use standard section headings: Experience, Education, Skills, Certifications.
- Mirror the exact job title (Data Intelligence Machine Learning Engineer) in your target role line where truthful.
- Quantify results with numbers, percentages, currency. Metrics improve ATS scoring and human review.
- Spell acronyms out once (e.g., Search Engine Optimization (SEO)) so parser catches both forms.
- Font: Arial, Calibri, or Times New Roman at 10-12pt. Fancy fonts can render as unreadable glyphs.
Top 10 Interview Questions for This Role
- Walk me through your background and why you’re interested in this role.
- Describe a challenging project you owned end-to-end. What was the outcome?
- Tell me about a time you disagreed with a stakeholder. How did you handle it?
- How do you prioritize when everything on your plate feels urgent?
- How do you decide between fine-tuning a model vs. prompt engineering?
- Explain overfitting to a non-technical stakeholder.
- Walk through your data validation pipeline for training data.
- How do you evaluate model performance beyond accuracy?
- Describe your MLOps setup — from experimentation to production.
- How do you handle model drift once a system is in production?
How to Apply
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