Applied AI Scientist at Echo Neurotechnologies
San Francisco, CA
$190,000–$240,000
Jul 25, 2026
San Francisco, CA
Jul 26, 2026
What this job asks for AI summary
This role sits at the intersection of neuroscience and machine learning, focused on building models that decode brain signals into control outputs for digital devices. The work involves designing transformer-based and other deep learning architectures trained on high-dimensional time-series neural data, then integrating those models into real-world assistive technology pipelines. It suits an experienced ML practitioner comfortable with large-scale distributed training and GPU-accelerated workflows.
Senior level · 5+ years · Bachelor's required · Full-time
Pay in the description: $190,000–$240,000
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Rare in this occupation — lead with these, and say what you built with them.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $122,874 (middle half $87,544–$162,374). This posting is about at that midpoint.
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (7)
No work location or city is specified in the posting; CBSA and state fields are left blank. The role appears to be on-site or hybrid at a startup, but remote is not offered — remote is set to false as no remote option is stated.
The title 'Applied AI Scientist' carries no explicit seniority level, so advertised seniority is Unspecified. The 5+ years requirement and scope (designing, deploying, and owning ML models end-to-end at an early-stage company) support a Senior classification.
The degree requirement states 'Bachelor's or Master's' with no 'or equivalent experience' escape clause, so Bachelor's is treated as the minimum hard requirement.
State-space models and foundation models appear in the same Required Qualifications bullet as time-series data but are framed as alternatives ('or'), so they are captured as separate preferred signals rather than independent hard gates alongside time-series data.
Medical/clinical application experience and translating scientific publications to datasets appear under the explicit 'Preferred Qualifications' heading.
The alt SOC 15-1252 (Software Developers) is noted because the role also involves deploying ML pipelines and integrating models into product applications, but the primary day-to-day work is clearly ML modeling and data science.
Ignored 1 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): medical/clinical ML applications.
Read the full posting
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