Machine learning engineer állások, munkák

• Development, adaptation, and deployment of end-to-end tracking solutions for sports officiating and analytics
• Implementation and optimization of ML models using Python, C++, PyTorch, NumPy, Pandas, and OpenCV
• Design and building of ETL pipelines and data manipulation workflows

• Design, development, and optimization of core ML models, especially in Computer Vision and Deep Learning
• Development of efficient, scalable training and inference pipelines
• Mentoring junior ML engineers, reviewing code and sharing best practices

• Architect and lead the development of large-scale ML systems and model pipelines
• Introduce new ML systems such as traffic sign detection, building facade detection, and other computer vision-based solutions
• Define and drive the ML technical roadmap in alignment with company objectives

• Design and build learner models including knowledge tracing and longitudinal approaches
• Shape the data foundation for learner modeling by defining relevant signals and building datasets
• Translate mastery and progression definitions into model targets and evaluation criteria

• Contribution to the ID Verification ML team building in-house ML solutions
• Delivery of high-quality NodeJS, TypeScript and Python code with simplicity, performance and scalability in mind

• Building and maintaining dataset ingestion, cleaning, and versioning pipelines
• Designing and operating labeling workflows including quality control and inter-annotator agreement tracking
• Owning dataset testing including coverage analysis, class balance, leakage detection, and dataset documentation

• Leading and mentoring a team of ML engineers
• Owning model quality end-to-end from experimentation to production-ready artifact
• Conducting regular 1-on-1s for career development and feedback
• Removing blockers and ensuring team well-being

• Designing, developing, and evaluating machine learning models and algorithms
• Working closely with data science peers and cross-functional stakeholders to translate business needs into actionable AI solutions aligned with strategic objectives

Design, train, and evaluate machine learning models for manufacturing use cases such as quality prediction, anomaly detection, predictive maintenance, and process optimisation, covering the full ML lifecycle from problem definition to performance tuning....