Machine learning engineer for medical devices

We are looking for a Senior Machine Learning Engineer / MLOps Engineer to own the end-to-end production lifecycle of our ML models for our medical device, Brain20. In this role, you will bridge the gap between initial research concepts and robust, production-ready systems. You will take ownership of taking algorithms from early-stage research/definition, refactoring and optimizing them, and deploying them onto both physical edge devices and AWS cloud infrastructure.

• Own the complete ML lifecycle—taking models from early-stage definition through refactoring, optimization, packaging, and deployment.
• Deploy, run, and optimize ML models on our physical medical device (Brain20) as well as AWS cloud infrastructure.
• Elevate experimental Python codebases into highly maintainable, efficient, concurrent, and thoroughly tested production software.
• Design, build, and maintain robust CI/CD pipelines and deployment strategies tailored for medical software standards.
• Translate experimental algorithms provided by neuroscience researchers into clean, high-performance, and scalable production code.

• 6+ years of professional software engineering experience in commercial environments (tech startups/scale-ups), with a strong track record of taking ML models into production.
• Proven ability to manage the complete lifecycle of ML models in production environments.
• Practical experience deploying models onto physical/embedded hardware (Edge AI) and AWS cloud infrastructure.
• Deep expertise in Python (concurrency, parallelization, memory management, profiling, and packaging).
• Clear theoretical understanding of machine learning fundamentals and deep learning architectures.
• Pragmatic & Product-Oriented
• Analytical Problem Solver
• Autonomous & Collaborative
• Fluent English
• Immediate or short notice period preferred
• Будет плюсом: Experience processing EEG, SSEP, or biosignal/time-series data; Familiarity with software development under medical device standards (e.g., ISO 13485, IEC 62304) or data privacy regulations (GDPR/HIPAA); Experience with model optimization techniques for edge devices (quantization, ONNX, TensorRT)

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