# Machine Learning Engineering Lead at Echo Neurotechnologies

> United States · Staff · ML / AI · open. Source: https://alderhq.app/jobs/greenhouse%3Aecho%3A4368052009 (read 2026-11-10).

## Facts (observed)

- Company: [Echo Neurotechnologies](https://alderhq.app/companies/echo-neurotechnologies)
- Location text: San Francisco; SF Office
- Where it can be done from (inferred): United States (evidence: "Location: San Francisco; SF Office")
- Posted: 2026-08-19 (82 days ago)
- First seen by alder: 2026-11-07 · last verified: 2026-11-07
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://job-boards.greenhouse.io/echo/jobs/4368052009
- Skills listed (inferred): Python, C++, Docker, PyTorch

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (SF Bay Area, n 174): p25 $248k, median $275k, p75 $300k

## Description (the employer's text)

### 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.

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Aecho%3A4368052009 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.