ML Infrastructure Engineer at Echo Neurotechnologies
San Francisco, CA
$180,000–$230,000
Jul 25, 2026
San Francisco, CA
Jul 26, 2026
What this job asks for AI summary
A senior infrastructure engineering role at an early-stage BCI startup, focused on designing and building cloud-based ML platforms, distributed training pipelines, and large-scale data infrastructure to support neural signal research and decoding models. The work spans architecting data ingestion and storage systems for high-dimensional time-series data through to enabling model experimentation, pretraining, and evaluation workflows. Best suited to engineers with deep experience in large-scale data pipelines and distributed ML training.
Senior level · 5+ years · Bachelor's required · Full-time
Pay in the description: $180,000–$230,000
“or” means any one of them counts — you don't need all of them.
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 $138,970 (middle half $107,524–$175,762). 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 are left blank. The role may be remote or at an unstated office — remote is marked false as the JD makes no explicit remote claim.
The Bachelor's degree requirement is a hard gate per the Qualifications section. An advanced degree (MS or PhD) is listed under 'Other Qualifications' and treated as preferred.
Distributed training frameworks (FSDP, DeepSpeed, Megatron-LM, Ray) are listed as examples in the required qualifications block; the capability is a hard gate but the specific tool is interchangeable — captured as a single skill with alternatives.
C++, Go, CUDA, Rust, and Java appear together under 'Other Qualifications' (the preferred/nice-to-have section) and are treated as preferred. They are grouped as alternatives since the JD presents them as a set of interchangeable options.
Kubernetes and Docker appear only under 'Other Qualifications' and are treated as preferred.
Time-series data experience and deep distributed-systems fundamentals also appear only under 'Other Qualifications' and are treated as preferred.
The role sits at the boundary between Software Developers (15-1252) and Database/Data Architects (15-1243) given its equal emphasis on ML infrastructure and large-scale data platform design; 15-1252 was chosen as primary because the JD centers on building and shipping ML systems and training pipelines, not purely data architecture.
Read the full posting
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