Informacje o stanowisku

Join our dynamic team working on a cutting-edge advertising platform that reaches millions of users globally. As a Machine Learning Engineer, you’ll contribute to the design and development of scalable, high-performance ML pipelines and infrastructure. Youll collaborate with top-tier engineers and researchers to bring real-world impact through applied AI at scale.

✅ What You Need to Succeed

• 2+ years of experience in machine learning or data science roles
• Strong Python skills (incl. pandas, scikit-learn) and knowledge of SQL (preferably Snowflake)
• Experience with ML libraries/frameworks: TensorFlow, PyTorch, Scikit-learn, PySpark
• Strong grasp of data analysis, statistics, and model evaluation
• Good understanding of CI/CD, version control (Git), and software engineering practices
• Ability to communicate technical insights to product teams and stakeholders
• Openness to learn and work in a cloud-based environment

⭐ Bonus Points for

• 5+ years of professional experience in ML/DS
• Experience with advertising, recommender systems, or RTB platforms
• Hands-on with AWS ML tools (e.g. SageMaker, Airflow), Snowflake, Redis, Aerospike
• MLOps & infrastructure experience: Docker, Kubernetes, Terraform, GitHub Actions, Prometheus, Grafana
• Previous technical leadership or mentoring experience
• Experience deploying ML models to production environments

Join our dynamic team working on a cutting-edge advertising platform that reaches millions of users globally. As a Machine Learning Engineer, you’ll contribute to the design and development of scalable, high-performance ML pipelines and infrastructure. Youll collaborate with top-tier engineers and researchers to bring real-world impact through applied AI at scale.

,[Develop and maintain scalable, low-latency ML pipelines and platforms, Build and optimize models using real-time and batch processing for billions of predictions daily, Collaborate with Data Scientists and MLOps teams on model lifecycle — from training to deployment and monitoring, Create proof-of-concept prototypes, experiment with new platforms, and support continuous integration and delivery, Streamline testing (unit, integration, stress), versioning, and automation of ML solutions, Research and apply the latest technologies in ML infrastructure, If youre experienced — youll also get a chance to lead projects and mentor others Requirements: Machine learning, Python, pandas, PyTorch, scikit-learn, PySpark, Data analysis, Git, SQL, Snowflake, TensorFlow, AWS, Airflow, Redis, Aerospike Database, MLOps, Docker, Kubernetes, Terraform, GitHub, Prometheus, Grafana Tools: Agile, Scrum. Additionally: Sport subscription, Private healthcare, International projects.

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