Senior Machine Learning Engineer

Job Description

We are seeking a Senior Machine Learning Engineer to design, scale, and deploy clinical-grade ML algorithms in the cloud. In this high-impact individual contributor role, you will take ownership of translating complex, neurophysiological signals into production-ready predictive models and automated diagnostic features. You will build and deploy cloud-based models, ensuring data is processed with high reliability, low latency, and strict regulatory compliance.

Role Responsibilities

• Algorithm Productionization & Cloud Deployment: Design, build, and deploy scalable ML pipelines and clinical algorithms in the cloud to process real-time and batch neural and contextual data.
• Advanced Model Architecture & Decoding: Design and build state-of-the-art transformers and deep learning models to decode complex neurophysiological signals into real-time control streams for digital devices.
• Model Optimization & Validation: Train, evaluate, and optimize machine learning models for signal processing, feature extraction, and behavior detection while maintaining high sensitivity and specificity.
• Model Deployment & Pipeline Integration: Architect scalable pipelines to deploy and integrate production models into the cloud environment, managing model automated training, data versioning, and performance monitoring.
• Cross-Functional Collaboration: Work closely with data scientists, software & firmware engineers, clinical researchers, and regulatory specialists to translate research-validated algorithms into commercial software features.
• Quality & Regulatory Compliance: Author software design specifications, risk analyses, and validation protocols to support FDA submissions under strict quality management systems.

Role Qualifications

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Biomedical Engineering, or equivalent practical experience.
• 5+ years of software engineering experience focusing on building, deploying, and maintaining production machine learning models in cloud environments.
• Strong proficiency in Python and modern ML frameworks (e.g. PyTorch) along with experience in cloud-native technologies (e.g. Docker).
• Solid foundation in digital signal processing (DSP), time-series analysis, or processing high-dimensional biological/medical sensor data.
• Demonstrated understanding of data privacy, security standards (HIPAA, SOC 2), and medical device software lifecycles (IEC 62304, ISO 13485).
• Excellent communication skills and a track record of driving technical execution independently in a fast-paced environment.

Preferred Qualifications

• Experience developing Software as a Medical Device (SaMD) or clinical decision support algorithms.
• Hands-on experience with MLOps tools such as Weights & Biases.
• Familiarity with streaming data platforms for real-time sensor processing.
• Experience writing Python/C++ bindings or optimizing cloud algorithm latency for real-time applications.