Research Scientist, Data Analysis

The role

This is the person who turns our experiments into results.

Arbor is generating novel datasets. In humans, we image deep brain structures with functional ultrasound, and we will soon be adding ultrasound neuromodulation. Those studies are longitudinal and causal: intervene, image the circuit response, track the symptom, and repeat in the same person over months. In animals, we combine this bidirectional ultrasound with electrophysiological stimulation and recording, giving us causal measurements across a wide range of spatial and temporal scales, in the same subjects over time.

Your primary responsibility is the analysis of those data and the tooling around them, working with a highly quantitative experimental team from the design stage onward. Because these studies will be longitudinal and closed-loop, there will also be a need for optimal experiment design, online analysis implementations, and closed-loop control.

Posted pay

$183K–$210K

a year

Compared with similar jobs

This base pay is below the median for AI research jobs in the Bay Area with no level in the job title.

Median: $238K a year
Other jobs in the group: 525

We compare the middle of each posted range. This is base pay as posted. The group does not include this job.

Location

San Francisco, CA

Work type

Onsite

Level

Not given

Posted

Sep 3, 2026

Last check

6 h ago

Job type

Research, Inference & ML systems, ML / AI engineering

The role

This is the person who turns our experiments into results.

What you will do

Own analysis of Arbor's experimental data, including functional ultrasound imaging and electrophysiology, along with the tooling that supports it
Work with the experimental team from study design through interpretation, so we answer the highest-priority questions
Develop image processing tools: registration and alignment, artifact handling, and whatever else the data demands
Help design algorithms for intervention optimization and adaptive experiment design
Develop online and real-time analysis methods to support closed-loop experiments
Build methods for integrating data across modalities and scales
Contribute to high-level scientific decisions about where the organization is going

Not all of this is one person's job. Where you start depends on your strengths and on what the program needs most.

Who We’re Looking For The successful candidate will have:

A strong quantitative background: a PhD, or comparable training in an equally demanding environment such as industry
Real depth in several of: signal processing, statistics and inference, machine learning, medical or brain imaging analysis, control theory, computational neuroscience; we don't expect all of them
Strong coding skills
A significant body of independent work you can point to
The judgment to tell a problem that can be solved with standard statistical methods from one that requires a more sophisticated approach – and the discipline to prefer the simpler answer
A self-starter with the skills and instinct to find the shortest effective path to the scientific goal, rather than the most interesting one
Directness without friction – you say what you think, ask when you don't know, and aren't looking to compete with your colleagues

Nice to have: experience in a professional software development environment; a public record of algorithm or code development on GitHub or similar; experience with ultrasound, neural, or medical imaging data; prior work in a research setting where you had to build the tooling yourself.

You don't need a neuroscience background. You do need to want one. Some of the best people to have worked on these problems originally came from another field.

Research Scientist, Data Analysis

The role

Research Scientist, Data Analysis

The role

What you will do

(formerly Forest Neurotech)

Pay and benefits

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Interview questions

Convergent Research

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