Neuronify

Embeddings

Numeric vectors that represent the meaning of text, images, or other data, so that similar items sit close together and can be found by distance.

Hiring data as of November 4, 2026 · refreshed daily

companies naming it
5 of 47companies naming it
open roles naming it
8open roles naming it
of those based in NYC
5of those based in NYC
pay: under 8 posted ranges
—pay: under 8 posted ranges

What Embeddings means

An embedding is a list of numbers produced by a model to represent a piece of content, arranged so that items with similar meaning end up near each other. Two sentences that say the same thing in different words get similar vectors, which is what makes search by meaning possible.

Embeddings are the foundation of semantic search, retrieval for RAG, clustering, deduplication, and many recommendation systems. Most teams use a provider’s embedding model rather than training one.

Embeddings in New York job posts

5 of the 47 New York AI companies we track name Embeddings in at least one open role today, across 8 postings. 5 of those are based in New York; the rest are remote or in the companies’ other offices.

This is engineering vocabulary: 75% of the postings that name it are Engineering roles.

How to read it

Named far less often than RAG or vector search, which is telling: employers ask for the system, not the component. When embeddings are called out specifically, the role usually involves search, ranking, or matching as a core product feature.

Go deeper: how retrieval and source-grounded answers work in practice, in the Neuronify AI Field Guide.

What these roles pay

Not enough data to publish. We need at least eight distinct posted salary ranges on New York roles naming Embeddings before showing a median, and today there are fewer. For posted pay by function and seniority across the whole market, use the NYC salary calculator or the NYC AI engineer salary guide.

Companies naming Embeddings, daily

Count of companies with at least one open role naming the term. We read between 46 and 47 companies a day over this period, so a move of one or two is noise.

05105Sep 20Nov 4Sep 20: 5 companiesSep 21: 5 companiesSep 22: 5 companiesSep 23: 5 companiesSep 24: 4 companiesSep 25: 4 companiesSep 26: 4 companiesSep 27: 4 companiesSep 28: 4 companiesSep 29: 5 companiesSep 30: 5 companiesOct 1: 6 companiesOct 2: 6 companiesOct 3: 6 companiesOct 4: 6 companiesOct 5: 6 companiesOct 6: 6 companiesOct 7: 6 companiesOct 8: 6 companiesOct 9: 6 companiesOct 10: 6 companiesOct 11: 6 companiesOct 12: 6 companiesOct 13: 7 companiesOct 14: 7 companiesOct 15: 7 companiesOct 16: 6 companiesOct 17: 6 companiesOct 18: 6 companiesOct 19: 7 companiesOct 20: 7 companiesOct 21: 6 companiesOct 22: 6 companiesOct 23: 6 companiesOct 24: 6 companiesOct 25: 6 companiesOct 26: 5 companiesOct 27: 5 companiesOct 28: 5 companiesOct 29: 5 companiesOct 30: 5 companiesOct 31: 5 companiesNov 1: 5 companiesNov 2: 5 companiesNov 3: 5 companiesNov 4: 5 companies
View as table
Datecompanies
2026-09-205
2026-09-215
2026-09-225
2026-09-235
2026-09-244
2026-09-254
2026-09-264
2026-09-274
2026-09-284
2026-09-295
2026-09-305
2026-10-016
2026-10-026
2026-10-036
2026-10-046
2026-10-056
2026-10-066
2026-10-076
2026-10-086
2026-10-096
2026-10-106
2026-10-116
2026-10-126
2026-10-137
2026-10-147
2026-10-157
2026-10-166
2026-10-176
2026-10-186
2026-10-197
2026-10-207
2026-10-216
2026-10-226
2026-10-236
2026-10-246
2026-10-256
2026-10-265
2026-10-275
2026-10-285
2026-10-295
2026-10-305
2026-10-315
2026-11-015
2026-11-025
2026-11-035
2026-11-045

Who names it

Open roles naming Embeddings, by company.

  • EvolutionIQ3
  • Pinecone2
  • Clay1
  • Headway1
  • Tabs1

Which roles

The same postings, by function and by seniority.

  • Engineering6
  • Product1
  • Research1
  • Senior3
  • Entry2
  • Mid2
  • Staff/Principal1

Named alongside Embeddings

Terms that appear in the same postings far more often than chance would put them there. The number is how many of the 5 companies pair the two.

Open roles naming Embeddings

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.

Embeddings — common questions

What does Embeddings mean in an AI job posting?

Numeric vectors that represent the meaning of text, images, or other data, so that similar items sit close together and can be found by distance. Named far less often than RAG or vector search, which is telling: employers ask for the system, not the component. When embeddings are called out specifically, the role usually involves search, ranking, or matching as a core product feature.

How many New York AI companies are hiring for Embeddings?

As of November 4, 2026, 5 of the 47 New York AI companies we track name Embeddings in the description of at least one open role, across 8 postings (5 based in New York). The count refreshes daily from the companies’ own job boards.

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

We do not publish a pay figure for Embeddings yet. Fewer than eight New York postings naming it carry a distinct posted salary range, and below that number a median says more about two or three employers than about the market.

Which skills are asked for alongside Embeddings?

In the same postings, the terms most distinctively paired with Embeddings are Vector search, RAG, Tool use, Evals, Machine learning. 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.

Need someone who has actually shipped this?

A keyword in a posting is easy to match and hard to verify. We match companies with AI engineers and consultancies vetted on production work, and tell you when the project needs a different skill than the one you named.

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