Neuronify

RAG (retrieval-augmented generation)

Fetching relevant documents at question time and putting them in the model’s context, so answers come from your data instead of the model’s memory.

Hiring data as of September 20, 2026 · refreshed daily

companies naming it
20 of 47companies naming it
open roles naming it
31open roles naming it
of those based in NYC
18of those based in NYC
median posted base (n=16)
$238kmedian posted base (n=16)

What RAG means

Retrieval-augmented generation answers a question in two moves. First, search a body of documents for the passages most relevant to the question. Second, give those passages to a language model along with the question and have it answer from them. The model’s training data stops being the source of truth; the retrieved text is.

It is the standard way to make a model useful over private or fast-changing information without retraining it. The hard parts are almost all on the retrieval side — how documents are chunked, how relevance is scored, how permissions carry through — which is why RAG work often looks more like search engineering than like machine learning.

RAG in New York job posts

20 of the 47 New York AI companies we track name RAG in at least one open role today, across 31 postings. 18 of those are based in New York; the rest are remote or in the companies’ other offices.

This is engineering vocabulary: 81% of the postings that name it are Engineering roles.

How to read it

A posting that names RAG is telling you the product answers questions over a customer’s documents, and that retrieval quality is a live problem. It pairs closely with vector search and evals in the same descriptions. Expect to be asked how you would measure whether the right passages were retrieved, separately from whether the final answer read well.

What these roles pay

$178k25th percentile
$238kMedian
$255k75th percentile

Midpoints of 16 distinct salary ranges posted on New York roles that name RAG. Base salary only; on-target earnings are excluded, and a range repeated across one company’s postings counts once. For comparison, the median across all New York engineering postings is $215k. A term’s pay reflects the roles that name it as much as the skill itself — see the role mix below before reading a premium into it. Full bands by function and seniority are in the NYC salary calculator.

Trend

We began extracting this term from posting text on September 20, 2026. Job descriptions disappear when a role is filled, so the series cannot be backfilled; a trend line appears here once a week of data exists.

Who names it

Open roles naming RAG, by company.

Also: EliseAI, H1, Hyperscience, Maven Clinic, Mirage (fka Captions), Normal Computing, Patlytics, SmarterDx.

Which roles

The same postings, by function and by seniority.

  • Engineering25
  • GTM3
  • Operations1
  • Product1
  • Design1
  • Mid13
  • Staff/Principal11
  • Senior5
  • Manager1
  • Entry1

Named alongside RAG

Terms that appear in the same postings far more often than chance would put them there. The number is how many of the 20 companies pair the two.

Open roles naming RAG

A sample from today’s data, New York roles first and one per company before any repeats. Links go to the employer’s own posting. For the full market, see AI jobs in NYC.

How this is counted

Every day we read the open roles on the public job boards of the New York AI companies in our coverage — the NYC AI 100 and the AI in NYC Show roster. This term is matched against each role’s description after removing the text a company repeats across its postings, so an “About us” paragraph cannot tag every role the company has open. A vendor’s own postings never count toward its own name.

The figure means named in a job posting. It does not mean used in production, and a “nice to have” counts the same as a requirement. Companies are the headline number because posting counts are dominated by whichever few employers are hiring hardest this month.

Full method, including the pay rules, is on the glossary index; the underlying series is the NYC AI Hiring Index.

RAG — common questions

What does RAG mean in an AI job posting?

Fetching relevant documents at question time and putting them in the model’s context, so answers come from your data instead of the model’s memory. A posting that names RAG is telling you the product answers questions over a customer’s documents, and that retrieval quality is a live problem. It pairs closely with vector search and evals in the same descriptions. Expect to be asked how you would measure whether the right passages were retrieved, separately from whether the final answer read well.

How many New York AI companies are hiring for RAG?

As of September 20, 2026, 20 of the 47 New York AI companies we track name RAG in the description of at least one open role, across 31 postings (18 based in New York). The count refreshes daily from the companies’ own job boards.

What do roles that ask for RAG pay in New York?

Across 16 distinct posted salary ranges on New York roles naming RAG, the median midpoint is $238k base, with the middle half between $178k and $255k. The same figure for all New York engineering postings is $215k. These are employer-posted ranges required by New York’s pay transparency law, base salary only.

Which skills are asked for alongside RAG?

In the same postings, the terms most distinctively paired with RAG are Vector search, MLOps, Prompt engineering, Fine-tuning, Evals. We rank pairings by how much more often they appear together than apart, so near-universal terms such as Python do not crowd out the informative ones.

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