Machine Learning Engineering Lead
Senior ML Engineer at Echo Neurotechnologies will design and create ML models for time-series data to develop Brain-Computer Interface solutions for people with disabilities, using Python with TensorFlow or PyTorch frameworks.
- Compensation
- $170K–$220K / yr
- Location
- San Francisco
- Work setup
- Not specified
- Seniority
- Lead
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- Bachelor's or Master's degree in Math, Engineering, Data Science, or other relevant quantitative fields
- Minimum of 5 years of combined academic and professional ML experience
- Proficiency in Python and either tensorflow or pytorch (pytorch is preferred)
- Proficiency in using GPUs and GPU software toolkits to accelerate ML pipelines
Nice to have
- PhD in a relevant quantitative field
- Both academic and industry ML experience
- Publication history in ML-related academic projects
- Experience working with time-series data from human participants or other physiological time-series data
Full description
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.