Applied ML Researcher

• Applied research position, acting as a bridge between the Algorithms and Development teams.
• Suitable for candidates who enjoy combining Algorithmics, Development, and Data Science.
• Involves model evaluation and deployment to production.
• Applied research, development, and maintenance of ML models in production.
• Full ownership over inference stages, optimization, and deployment of existing models.
• Development of advanced pipelines for model serving, including integration of ONNX and TensorRT.
• Responsibility for CI/CD processes and performance monitoring of models in production.
• End-to-end ownership of Model Lifecycle infrastructure.
• Daily work with Deep Learning models for Computer Vision and Signal Processing.
• Includes Fine-tuning, Benchmarking, Data Processing, and infrastructure development for monitoring and control.
• Emphasis on Reproducibility and Scalability.
• Hands-on work with various production-grade DL models as part of an agile, small team.
• Tech stack includes: Python, Docker, Git, Linux, PyTorch, TensorFlow, SQL/NoSQL, Computer Vision, Signal Processing, AI/DL, and GCP.

• B.Sc. in a scientific field from a recognized university - required (M.Sc. - strong advantage)
• 5 years of experience as an Algorithm Developer / ML Engineer / similar titles - required
• 3 years of hands-on experience with Python - required
• 3 years of experience working with Computer Vision models - required
• Experience with classical Machine Learning models - required (e.g., Ridge, Lasso, Random Forest)
• Experience with at least one of the following: PyTorch / TensorFlow / PyTorch Lightning - required
• Experience with MLOps - required
• Experience working in a cloud environment - required
• Experience with Deep Learning - advantage
• Experience with Signal Processing - advantage

Mploy