Senior Data Intelligence Machine Learning Engineer

About Dyson and the Role

Dyson is a leading innovator in engineering, AI, and robotics dedicated to transforming future technology through data-driven solutions. The Data Intelligence team plays a pivotal role in advancing connected product development by crafting comprehensive data pipelines and strategies. This environment encourages creativity, experimentation, and impactful delivery, leveraging collaboration with global engineering teams and external partners.

We seek a Senior Data Intelligence Machine Learning Engineer specializing in automating data annotation processes. The focus is on minimizing manual labeling efforts by applying methods such as Active Learning, Weak Supervision, and Synthetic Data generation. The role bridges raw data acquisition with model-ready labeled datasets, maintaining scalable high-quality labeling standards.

Key Duties

• Design and implement comprehensive automated labeling systems using platforms like Snorkel, Cleanlab, or custom active learning frameworks.
• Develop Human-in-the-Loop (HITL) mechanisms allowing models to pre-label datasets while humans review uncertain samples.
• Implement algorithms to identify and rectify noisy or incorrect labels within datasets to enhance data quality.
• Collaborate with software engineering teams to integrate labeling tools with data lakes and machine learning infrastructure.
• Tune teacher models to create precise pseudo-labels supporting the training of student models.
• Establish and maintain scalable data preparation workflows optimized for quality and efficiency within MLOps pipelines.
• Utilize advanced data visualization and feature engineering techniques to convert raw data into insightful analytics supporting research and deployment.
• Partner closely with Data Scientists, Software Engineers, and Product teams to ensure data consistency and usability across projects.

Candidate Profile

• Minimum of five years’ experience in machine learning engineering, particularly in data-centric AI or computer vision/NLP data pipelines.
• Expertise in Python programming and proficiency with major ML frameworks such as PyTorch or TensorFlow along with libraries like NumPy, Pandas, and Scikit-learn.
• Specialized knowledge in automated labeling applying Weak Supervision (labeling functions) or Active Learning techniques like uncertainty or diversity sampling.
• Experience managing large unstructured datasets (images, text, audio) via SQL and NoSQL databases.
• Competence with cloud ML labeling services including AWS SageMaker Ground Truth, GCP Vertex AI, or Azure ML.
• Familiarity with data version control tools such as DVC to manage dataset changes over time.
• Proven record building auto-labeling workflows or managing extensive data annotation pipelines.
• Solid skills in data pipeline design, implementation, cleansing, transformation, and storage across cloud, on-premises, or hybrid architectures.
• Advanced capabilities in feature engineering, exploratory data analysis, and visualization tools like Jupyter, Tableau, or Power BI.
• Effective communication skills to document solutions clearly and foster collaboration among multidisciplinary teams.
• Ability to deliver solutions swiftly without compromising quality while staying informed on latest ML advancements.
• Holding a Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Data Science, or related disciplines.

Diversity and Inclusion

Dyson is committed to equal opportunity employment and embraces diverse perspectives to enhance technological innovation. All hiring decisions are made free from discrimination regarding race, color, religion, national origin, gender, sexual orientation, age, disability, veteran status, or any other protected characteristic.