# Applied AI Engineer, Evals at Sable

> Onsite · United States · Unspecified · ML / AI · open. Source: https://alderhq.app/jobs/ashby%3Asable%3A36addef7-4840-4a5e-bbcb-69502776801c (read 2026-11-07).

## Facts (observed)

- Company: [Sable](https://alderhq.app/companies/sable)
- Location text: San Francisco
- Where it can be done from (inferred): Onsite · United States (evidence: "Location: San Francisco")
- Posted: 2026-09-15 (52 days ago)
- First seen by alder: 2026-11-02 · last verified: 2026-11-03
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://jobs.ashbyhq.com/sable/36addef7-4840-4a5e-bbcb-69502776801c

## 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)

### About Sable

Sable built Aidan, the first AI employee who can lead customer calls using realtime voice, vision, and browser use. Aidan runs a live, two-way conversation inside a real product environment, clicking through the product like a human, watching the user's screen, and adapting the journey on the fly. Every conversation feeds a self-improving context graph we call the Brain, so Aidan gets smarter with each call.

### The role

You translate what makes a good performance into clear, defensible signals, and use those signals to improve Aidan. Role involves carrying out evaluations of agents on a broad range of areas on the frontier including voice, vision, and browser use, and using the findings to optimize the agent, context, and models.

### What you'll do

Design evaluations for voice, vision, and browser use to direct and accelerate improvements in the agent, context, and models

Build and maintain our internal benchmarks and simulations

Work on systems to score customer-specific scenarios and identify gaps in current capabilities.

### Who you are

Engineer with statistics and/or applied machine learning experience

Comfortable in a production codebase

Bonus: prior experience with evaluations or with voice, realtime, or browser use/computer use agents.

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Asable%3A36addef7-4840-4a5e-bbcb-69502776801c and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.