Machine Learning Engineer

Pirelli

Slatina, Romania

DRIVE YOUR FUTURE

This role is well-suited for individuals with a technical background, particularly in industrial engineering. The ideal candidate should be analytical, detail-oriented, and capable of handling complex data. At Pirelli you will apply statistical analysis and machine-learning techniques to a wide range of data (from sensors, to telemetry, to manufacturing) and you will change how decisions are made across all business units. You will oversee deploy and manage ML solutions and data architecture of an entire factory, collaborating with HQ data science team and local people to help increase the knowledge and the efficiency of the plant.

YOUR CORE ACTIVITIES

Monitor ML processes and performances and their impact on the factory;
Tune such models and adapt them continuously;
Explore data, monitoring and helping increase their quality;
Facilitate the communication between HQ team and the plant workers.

WHAT WE ARE LOOKING FOR

Master’s in Computer Science, Physics, Math, Engineering, Statistics or any other data-oriented or coding related disciplines;
PhD is a plus. Good knowledge of machine learning (ML) models;
Experience with ML model deployment, lifecycle management, and monitoring in production environments (MLOps);
Strong programming skills in Python with advanced knowledge of TDD (Test-Driven Development), OOP (Object-Oriented Programming), and software engineering best practices;
Data visualization expertise;
Advanced knowledge of SQL;
Familiarity with deep learning frameworks such as TensorFlow, PyTorch, or Keras;
Experience with GIT and collaborative development tools (e.g., GitHub, GitLab, Bitbucket);
Good communication skills and empathy, Curiosity;
Proficiency in data visualization (e.g., Matplotlib, Seaborn, Plotly, or Dash);
Knowledge of CI/CD;
Experience with Docker, with deep learning, Apache Spark;
Working experience with AWS cloud framework and services;
Understanding of model governance, data versioning (e.g., DVC, MLflow), and reproducibility;
Hands-on experience with AWS and its services for data science/ML (e.g., S3, SageMaker, EC2, Lambda, Glue).