# ML Infrastructure Engineer at Echo Neurotechnologies

> United States · Unspecified · ML / AI · open. Source: https://alderhq.app/jobs/greenhouse%3Aecho%3A4320966009 (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: 2026-07-17 (115 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/4320966009
- Skills listed (inferred): Python, PyTorch

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (SF Bay Area, n 461): p25 $200k, median $250k, 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.

### SUMMARY

We are seeking a Senior Machine Learning Infrastructure Engineer to join our team. The person who fills this role will design, build, and scale infrastructure to power massive-scale data, modeling, and analysis platforms, playing a critical role in shaping a high-performance, production-grade ML ecosystem to support rapid experimentation with diverse datasets spanning neural signals, behavior, and more. This person will have significant ownership over the ML R&D platform, working closely with domain experts to architect new cloud infrastructure, data pipelines, and modeling flows. The work will ultimately enable the development of cutting-edge models for neuroscientific discovery and neural decoding, empowering brain-computer interface technology to improve the lives of patients living with severe neurological conditions.

### PRIMARY ROLE AND RESPONSIBILITIES

- Create flexible and performant ML infrastructure
- Design and build systems ML cloud infrastructure to enable massive-scale modeling and analytics
- Support diverse model exploration, hyperparameter optimization, pretraining, fine-tuning, and evaluation processes
- Design and optimize scalable distributed training pipelines, with support for features such model sharding, cross-GPU communication, and real-time training monitoring
- Create, operate, and maintain robust ML platforms and services across the model lifecycle
- Make informed architecture decisions that balance performance, cost, reliability, and scalability
- Build diverse and scalable data platforms
- Design, build, and optimize massive-scale databases and data pipelines for scalable, flexible, and reliable data access
- Explore research-driven, tailored data solutions using existing and simulated data, comparing performance and efficiency across solutions for typical data-access patterns
- Create infrastructure and pipelines for ingesting internal and external datasets with varied shapes, formats, and associated metadata
- Design and assess custom data formats for efficient storage and slicing of high-dimensional time-series data
- Enable efficient data movement, preprocessing, and artifact management for data lineage and modeling reproducibility
- Meet company standards for delivered solutions
- Establish best practices for reliability, observability, reproducibility, and operational excellence across the ML ecosystem
- Make informed and collaborative decisions with domain experts across the software & ML teams
- Foster visibility and reproducibility within the company by maintaining extensive documentation of design decisions, evaluations of viable alternatives for selected solutions, pipeline assessments, etc.
- Support ML R&D operations while preparing for eventual incorporation into product pipelines
### QUALIFICATIONS

- Bachelor's degree in Computer Science, Electrical Engineering, or a related technical discipline
- 5+ years of industry experience in software engineering, large-scale data infrastructure, or systems ML
- Extensive proficiency in Python
- Familiarity with PyTorch
- Experience designing, building, and maintaining high-throughput data pipelines for large and diverse datasets
- Experience working with distributed-training frameworks (e.g. FSDP, DeepSpeed, Megatron-LM, Ray, etc.)
- Experience building or optimizing ML training pipelines for transformers or other large neural-network models
- Demonstrated ability to partner closely with research and modeling teams to productionize workflows

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Aecho%3A4320966009 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.