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)

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 Availability:

Immediate or short notice period preferred

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📌 Senior Machine Learning Engineer (MLOps & Edge/Cloud) (Barcelona)
🏢 Time is Brain
📍 Barcelona