Data Intelligence Machine Learning Engineer
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Key skills for this role
About the Role
Dyson seeks a Data Intelligence Machine Learning Engineer to design and implement automated data labelling pipelines using techniques like Active Learning and Weak Supervision. Requires 3+ years ML engineering experience, proficiency in Python, and expertise in data-centric AI.
Key Skills for This Role
Responsibilities
- Design and deploy end to end automated labelling systems using frameworks like Snorkel, Cleanlab, or custom active learning loops
- Develop Human in the Loop systems where models pre label data and humans intervene on high uncertainty samples
- Implement algorithmic checks to identify and correct mislabelled or noisy data
- 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
- Set up and maintain robust data preparation infrastructure for data quality and speed
- Perform data visualization and in depth analysis using advanced data and feature engineering techniques
- Work closely with Data Scientists, Software Engineers, and Product teams to ensure high data quality
Requirements
- At least 3+ years of professional experience in Machine Learning engineering
- Proficiency in Python and ML stack (PyTorch or TensorFlow, NumPy, Pandas, Scikit learn)
- Experience with Weak Supervision or Active Learning strategies
- Experience with SQL and NoSQL databases, managing large scale unstructured data
- Familiarity with cloud infrastructure (AWS, GCP, or Azure ML)
- Experience with DVC or similar data version control tools
- Bachelor's or Master's degree in computer science, Engineering, Mathematics, Data Science, or related field
Full Job Posting
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.
- You’ll work alongside brilliant minds from Dyson global engineering team and external software/hardware partners.
About the role
- We are looking for a specialized 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.
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.
- 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 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.
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