Research Engineer

Hi, I'm Brian, Co-Founder of Egra. We just raised $5.5M to build foundation models for brain signals, and We're looking for research engineers to join our founding team.

You'll have complete ownership over your work from day one. No lengthy onboarding, no waiting for permission, no navigating layers of approval. A small founding team, deep technical problems, and the resources to solve them. You'll define the infrastructure architecture, make critical engineering decisions, and build the systems that make our research possible. If you thrive with high agency and want your work to directly shape the company's trajectory, this is that opportunity.

EEG — electrical brain activity recorded from the scalp — is one of the hardest real-world signal modalities in ML: low signal-to-noise ratio, massive subject variability, and device inconsistencies. Most people avoid it for these reasons.

As our research engineer, you'd own the systems that make research possible. To ground it with real examples, the kind of projects you'd own:

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The majority of failed ML research fails because of infrastructure, not ideas. Bad data splits leak information. Preprocessing bugs silently invalidate months of experiments. Training runs can't be reproduced because no one tracked the right things. Results look great until someone realizes the evaluation was wrong.

EEG makes all of this worse. We're dealing with data from different devices, electrode layouts, and sampling rates. As we scale from public datasets to clinical partnerships to consumer data collection, the infrastructure has to handle all of it cleanly.

You'll be embedded in the research, not adjacent to it.

• Experience building ML training and data pipelines for real-world data

• Strong Python skills and comfort with the PyTorch ecosystem

• Experience with experiment tracking, data versioning, and reproducible workflows

• The ability to debug data and training issues that span the full stack, from raw signal to loss curve

• Experience with signal processing or time-series data pipelines

• Comfort with distributed training or mixed-precision optimization

• Having built internal tools that researchers actually loved using

• Familiarity with data formats like EDF, BIDS, or HDF5

• Experience with EEG/BCI data pipelines or neuroscience data tooling (MNE-Python, MOABB, Braindecode)

• You've only worked with clean, well-structured datasets

• You need detailed specs before you can start building

• You're not comfortable working in a 3–5 person team with no dedicated manager

Our process is three conversations:

1. 30-minute intro call. We'll tell you what we're working on, you'll tell us what you've worked on. Casual, honest, no prep needed.

2. 30-minute technical conversation. We'll work through a real infrastructure design problem together. No right answer. We want to see how you think about tradeoffs, correctness, and iteration speed.

3. 30-minute deep dive. You'll meet both founders. We'll dig into past projects, talk about how you debug hard data problems, and figure out if we'd enjoy working together every day.