Company Overview

Echo Neurotechnologies is an exciting new startup in the Brain-Computer Interface (BCI) space, driving innovation through advanced hardware engineering and AI solutions. Our mission is to deliver cutting-edge technologies that restore autonomy to people living with disabilities and improve their quality of life.

Team Culture

Join a small, dedicated team of knowledgeable and motivated professionals. Our early-stage environment offers the opportunity to take ownership of broad decisions with significant and long-lasting impact. We emphasize continuous learning and growth, fostering cross-functional collaboration where your contributions are vital to our success.

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.

What We Offer

An opportunity to work on exciting, cutting-edge projects to transform patients’ lives in a highly collaborative work environment.