Description review

Renaissance Philanthropy | Research Engineer (Speech AI/ML) | Remote (worldwide)

Renaissance Philanthropy · Remote (worldwide) · back to the listing

HR standards

37/100

poor

Title ↔ description

65/100

needs work

Reads as

Machine Learning Engineer

100% confident

What this role officially is

data scientist — ESCO, the EU occupation classification

Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.

Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist

How others title the same work

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What the listing never says

  • No section describes what the person would actually do. Scope clarity
  • No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency

The listing, marked up

Renaissance Philanthropy's aim is to activate a virtuous circle of increasing ambition and impact between philanthropists and innovators: by identifying frontier experts both in science and in new ways of solving problems; by tapping into the growing number of emerging philanthropists; and by building multi-sector initiatives that can harness the power of philanthropy, markets, and governments.
RenPhil's Engineering Hub supports mission-driven, grant-funded educational technology projects that seek to improve educational outcomes in fundamental skills like math and reading. This summer we're launching a program called LEVI Literacy, which has a goal of halving the number of struggling readers in the US within 5 years.
We're looking for an expert in modern voice AI to support this work. Our partner teams are building tools that assess and support early reading directly from children's speech in real classrooms — oral reading, spoken vocabulary tasks, whole-class instruction. These are hard, meaningful speech problems: young1 children's voices, noisy multi-speaker environments, and high stakes for getting it right — and fair — for every student.
We're looking for folks who:
- have built and operated production ASR or speech-processing systems
- know the full data-to-model pipeline: collection, annotation, fine-tuning, evaluation, and deployment
- can speak the languages of both research and engineering
- want to help create a world where all children learn to read well.
If this sounds like you, please apply!
https://www.renaissancephilanthropy.org/careers

How this was produced

Highlights are found by rule, not by a model: each one is a phrase matched at a known position, and every note is a template we wrote. The two scores come from a typed-decision model (Jev) that reads the listing against the official role definition and real listings for the same role, and returns probabilities rather than prose — it never writes any of the words on this page, and never chooses what to highlight.

Deterministic penalty applied to the HR score: 20 points (from 57 before penalties). Reviewed 21 Sep 2026.