Senior Machine Learning Engineer (MLOps & Edge/Cloud)

Position Overview 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. Key Responsibilities - End-to-End Production: Own the complete ML lifecycle—taking models from early-stage definition through refactoring, optimization, packaging, and deployment. - Edge & Cloud Deployment: Deploy, run, and optimize ML models on our physical medical device (Brain20) as well as AWS cloud infrastructure. - Code Optimization & Engineering: Elevate experimental Python codebases into highly maintainable, efficient, concurrent, and thoroughly tested production software. - CI/CD & MLOps Infrastructure: Design, build, and maintain robust CI/CD pipelines and deployment strategies tailored for medical software standards. - Model Translation: Translate experimental algorithms provided by neuroscience researchers into clean, high-performance, and scalable production code. Required Qualifications - 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. - Solid End-to-End ML & MLOps Experience: Proven ability to manage the complete lifecycle of ML models in production environments. - Hands-on Deployment (Edge + AWS): Practical experience deploying models onto physical/embedded hardware (Edge AI) and AWS cloud infrastructure. - Advanced Python Engineering: Deep expertise in Python (concurrency, parallelization, memory management, profiling, and packaging). - Solid ML Theory: Clear theoretical understanding of machine learning fundamentals and deep learning architectures. Preferred Qualifications (Nice to Have) - 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). Soft Skills - Pragmatic & Product-Oriented - Analytical Problem Solver - Autonomous & Collaborative - Fluent English Availability: Immediate or short notice period preferred