Machine Learning Engineering Lead

Echo Neurotechnologies · San Francisco

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Job Description

We are seeking a Machine Learning Engineering Lead to design, scale, and deploy clinical-grade ML algorithms in the cloud. In this role, you will initially operate as a high-impact individual contributor, and as the platform matures, you will expand and lead a dedicated team of machine learning engineers, shaping both the technical roadmap and day-to-day execution. You will take ownership of translating complex, neurophysiological signals into production-ready predictive models and automated diagnostic features. You will design, 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.
• 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, in collaboration with a platform team.
• Mentorship & Process: Introduce engineering best practices, including CI/CD pipelines, code reviews, and modular architecture, while coaching and mentoring engineers through regular feedback and technical guidance.
• Team Growth & Leadership: Build and scale a high-performing ML engineering team from the ground up, transitioning into a direct line management role to lead technical strategy, resource planning, and day-to-day execution.
• Quality & Regulatory Compliance: Produce rigorous technical documentation, software design specifications, risk analyses, and validation protocols to support regulatory 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 and security standards for handling PII and PHI (HIPAA, SOC 2).
• 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 under medical device software lifecycle regulations (IEC 62304, ISO 13485).
• 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.

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