Informacje o stanowisku

ML Engineers share fundamental responsibilities in streamlining machine learning project lifecycles. They are dedicated to designing and automating workflows, implementing CI/CD pipelines, ensuring reproducibility, and providing reliable experiment tracking. Their responsibilities also include collaborating with stakeholders and platform engineers, leveraging expertise in infrastructure setup, model deployment, monitoring, and proficiency with cloud platforms and data processing.

• Strong proficiency in Python with experience in the NumPy, Pandas, and SciPy stack
• Experience in writing advanced SQL queries
• Comfortable using Git
• Experience with containerization technologies like Docker
• Familiarity with Google Cloud Platform (or other cloud services)
• Understanding of fundamental statistical concepts (e.g., Bayes’ rule, linear regression, maximum likelihood estimation)
  
• Knowledge of Airflow, Kedro, and Kubernetes is a plus

ML Engineers share fundamental responsibilities in streamlining machine learning project lifecycles. They are dedicated to designing and automating workflows, implementing CI/CD pipelines, ensuring reproducibility, and providing reliable experiment tracking. Their responsibilities also include collaborating with stakeholders and platform engineers, leveraging expertise in infrastructure setup, model deployment, monitoring, and proficiency with cloud platforms and data processing.

,[Playing a critical role in developing new algorithms and optimizing existing ones, Adding significant pieces of functionality to the application, largely based on user feedback, Optimizing ML pipelines, Taking the initiative and proposing new approaches, Designing and architecting machine learning workflows/machine learning lifecycle process, Implementing ML workflows / automating CI/CD pipelines, Collaborating with Platform Engineers to set the infrastructure required to run MLOps processes efficiently Requirements: Python, NumPy, Pandas, SQL, Git, Docker, GCP, Airflow, Kedro, Kubernetes Tools: Jira, GitLab, GIT, Jenkins / GitLab, Agile. Additionally: Sport subscription, Private healthcare, Flat structure, Small teams, International projects, Team Events, Training budget, Free coffee, Gym, Bike parking, Playroom, Free snacks, Free beverages, In-house trainings, Startup atmosphere, No dress code, Kitchen.

Praca Warszawa

Warszawa - Oferty pracy w okolicznych lokalizacjach