Senior ML Engineer — Time-Series AI for Assistive Tech

Echo Neurotechnologies; San Francisco (CA); On-site; USD 170,000 - 220,000; Full time

Benefits offered by this job

Stock options

Comprehensive benefits package

401(k) with matching

Job summary

Echo Neurotechnologies is seeking an experienced Senior ML Engineer to translate time-series data into decoding models that empower people with disabilities to control a computer and communicate. You will work within a small, collaborative team on cutting-edge BCI hardware and AI software solutions.

The role focuses on building robust models, feature engineering, and integrating ML into product pipelines, with a strong emphasis on reproducible code and documentation for medical-grade products.

Qualifications

• Bachelor’s or Master’s degree in quantitative field.
• Minimum 5 years of ML experience (academic + industry).
• Proficiency in Python and PyTorch (TensorFlow also acceptable).
• Experience using GPUs and GPU toolkits to accelerate ML pipelines.

Responsibilities

• Design state-of-the-art ML models translating time-series data into control signals.
• Perform feature engineering to improve representations of datasets.
• Ensure robustness of decoding models for real-world deployment.
• Incorporate AI tools (e.g., LLMs) into research workflows.
• Design cloud-based ML systems and data pipelines for large-scale data science.

Skills

Time-series ML

Medical/clinical ML domain

Team collaboration

Clear communication

Education

Bachelor’s or Master’s in Math, Engineering, Data Science, or related field

PhD (preferred)

Tools

Python

PyTorch

TensorFlow

GPUs / CUDA

Job description

Echo Neurotechnologies is seeking an experienced Senior ML Engineer to translate time-series data into decoding models that empower people with disabilities to control a computer and communicate. You will work within a small, collaborative team on cutting-edge BCI hardware and AI software solutions.

The role focuses on building robust models, feature engineering, and integrating ML into product pipelines, with a strong emphasis on reproducible code and documentation for medical-grade products.