# General Expression of Interest - Neo Research at SASH

> Remote · Singapore · Unspecified · Other · open. Source: https://alderhq.app/jobs/ashby%3ASASH%3A56624cdc-897a-4a2d-a317-8b09bf6e4813 (read 2026-11-04).

## Facts (observed)

- Company: [SASH](https://alderhq.app/companies/sash)
- Location text: Singapore; Remote/Flexible
- Where it can be done from (inferred): Remote · Singapore (evidence: "Location: Singapore; Remote/Flexible")
- Posted: 2026-10-13 (22 days ago)
- First seen by alder: 2026-11-02 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://jobs.ashbyhq.com/SASH/56624cdc-897a-4a2d-a317-8b09bf6e4813
- Skills listed (inferred): LLMs

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (Singapore, n 15): p25 SGD 104k, median SGD 135k, p75 SGD 135k

## Description (the employer's text)

### Interested in working with Neo Research, but don't see the right role currently open?

At Neo Research (新衡), we're always interested in hearing from people who are excited about our work and could be a strong fit for future opportunities.

Submit an expression of interest below to tell us a little about your background, your research interests, and the kinds of questions you'd want to work on with us. We'll keep your information on file and may reach out if a relevant opportunity comes up.

We're particularly interested in research scientists and research engineers with backgrounds in model evaluations, alignment, interpretability, ML and LLM engineering, agentic systems, and adjacent fields whose methods transfer to frontier AI safety. You don't need to have worked in AI safety before. Reading and writing Mandarin is a plus.

This is not an application for a specific open position, and we may not be able to respond to every submission individually. For currently open positions, please apply directly through the relevant job posting.

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3ASASH%3A56624cdc-897a-4a2d-a317-8b09bf6e4813 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.