Lead Data Intelligence Machine Learning Engineer
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Key skills for this role
About the Role
Dyson is seeking a Lead Data Intelligence Machine Learning Engineer to design and implement automated data labeling pipelines using active learning, weak supervision, and synthetic data generation.
Key Skills for This Role
Responsibilities
- Architect and deploy end to end automated labeling systems using frameworks like Snorkel, Cleanlab, or custom active learning loops
- Build human in the loop (HITL) systems where models pre label data and humans intervene on high uncertainty samples
- Implement algorithmic checks to identify and correct mislabeled or noisy data within existing datasets
- Collaborate with software engineers to integrate labeling tools with existing 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 optimizing for data quality, speed, and integration with 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
Requirements
- 8+ years of professional experience in Machine Learning engineering, specifically focused on data centric AI or computer vision/NLP pipelines
- Proficiency in Python with mastery of ML stack (PyTorch or TensorFlow, NumPy, Pandas, Scikit learn)
- Proven experience with Weak Supervision or Active Learning strategies
- Experience with SQL and NoSQL databases, managing large scale unstructured data
- Familiarity with AWS (SageMaker Ground Truth), GCP (Vertex AI), or Azure ML labeling services
- Experience with DVC or similar tools for data version control
- Hands on expertise building auto labeling solutions or working with large scale data annotation workflows
- Experience designing, deploying, and maintaining scalable data pipelines
- Strong background in feature engineering, data analysis, and data visualization
- Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, Data Science, or related field
Full Job Posting
About Us
- Dyson is driven by innovation in engineering, AI, and robotics.
- The Data Intelligence team shapes Dyson's future through data, powering intelligent products.
- You'll work with Dyson global engineering and external partners in an environment built for exploration and impact.
About The Role
- Seeking a Lead Data Intelligence Machine Learning Engineer to design and implement in house tools for automated data labeling pipelines.
- Goal is to reduce reliance on manual annotation using Active Learning, Weak Supervision, and Synthetic Data Generation.
- Bridge raw data collection and model ready datasets, ensuring high quality labels at scale.
Key Responsibilities
- Architect labeling pipelines: design and deploy end to end automated labeling systems using frameworks like Snorkel, Cleanlab, or custom active learning loops.
- Develop Human in the Loop (HITL) systems: build interfaces where models pre label data and humans intervene on high uncertainty samples.
- Quality Assurance & Denoising: implement algorithmic checks to identify and correct mislabeled or noisy data.
- Tooling & Integration: collaborate with software engineers to integrate labeling tools with 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 optimizing for data quality, speed, and MLOps integration.
- 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.
About You
- At least 8+ years of professional experience in Machine Learning engineering, focused on data centric AI or computer vision/NLP pipelines.
- Proficiency in Python: mastery of ML stack (PyTorch or TensorFlow, NumPy, Pandas, Scikit learn).
- Automated labeling expertise: proven experience with Weak Supervision or Active Learning strategies.
- Data engineering: experience with SQL and NoSQL databases, managing large scale unstructured data.
- Cloud infrastructure: familiarity with AWS (SageMaker Ground Truth), GCP (Vertex AI), or Azure ML labeling services.
- Version control for data: experience with DVC or similar tools.
- Hands on expertise building auto labeling solutions or working with large scale data annotation workflows.
- Advanced skills in Python and key ML/data science libraries.
- Experience designing, deploying, and maintaining scalable data pipelines.
- Strong background in feature engineering, data analysis, and data visualization.
- Great communicator who documents solutions clearly and collaborates across teams.
- Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, Data Science, or related field.
What We Offer
- Dyson is an equal opportunity employer.
About The Company
- Searching, interviewing and hiring are all part of the professional life.
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