At the company, 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 the company'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 the company's global engineering team and external software/hardware partners in an environment built for exploration, discovery, delivery, and impact.
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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. 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—optimizing 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.
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 with mastery of the Machine Learning stack (PyTorch or TensorFlow, NumPy, Pandas, Scikit-learn).
- Automated Labelling Expertise: Proven experience with Weak Supervision or Active Learning strategies.
- Data Engineering: Experience with SQL and NoSQL databases, 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.
- Hands-on expertise building auto-labelling solutions or working with large-scale data annotation workflows.
- Strong background in feature engineering, data analysis, and data visualization with tools like Jupyter, Tableau, or Power BI.
- Great communicator who documents solutions clearly and collaborates effortlessly across technical and non-technical teams.
- Bachelor’s or Master's degree in computer science, Engineering, Mathematics, Data Science, or a related field.
Language Requirements:
English proficiency is required.
The company is an equal opportunity employer, welcoming applications from all diverse backgrounds without regard to race, colour, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, or other dimensions of diversity.
