Machine Learning Engineer

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. You'll collaborate with top-tier engineers and researchers to bring real-world impact through applied AI at scale.

🔍 What You’ll Be Doing

• 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 you're experienced — you'll also get a chance to lead projects and mentor others

✅ 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