Decagon

Scaling

Vertical AI · Customer support AI agents

Currently tracking 31 active AI roles, down 52% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $150k–$430k (avg $284k).

Hiring
31 / 31
Momentum (4w)
↓-14 -52%
13 opens last 4w · 27 prior 4w
Salary range · avg $284k
$150k–$430k
USD · disclosed roles only
Tracked since
Aug '25
last role today
Hiring velocityscroll left for older weeks
1 new role
Nov 11
1 new role
Apr 28
1 new role
May 5
2 new roles
Aug 18
1 new role
25
1 new role
Sep 22
2 new roles
Oct 27
2 new roles
Nov 10
2 new roles
17
2 new roles
Dec 1
2 new roles
8
2 new roles
Jan 12
7 new roles
19
5 new roles
26
6 new roles
Feb 2
5 new roles
9
3 new roles
16
9 new roles
23
1 new role
Mar 2
3 new roles
9
3 new roles
16
7 new roles
23
7 new roles
30
10 new roles
Apr 6
1 new role
13
3 new roles
20
3 new roles
27
6 new roles
May 4

Jobs (2)

31 AI · 98 total active
FilteredStageShip×
Show
Active onlyAI only (≥ 7)
Stage
AllPost-train · 4Serve · 1Agent · 29Ship · 2
Function
AllProduct · 55Engineering · 43
Country
AllUnited States · 80United Kingdom · 11Australia · 1Mexico · 1
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TitleStageFunctionLocationFirst seenAI score
Director of Solutions Engineering, Enterprise West
Director of Solutions Engineering for a conversational AI platform, focusing on pre-sales technical leadership, team management, and driving enterprise adoption of AI solutions. This role involves engaging with complex enterprises, designing technical strategies, and collaborating with sales, product, and engineering teams.
ShipEngineeringSan Francisco, CA8w ago7
Senior Data Scientist
Decagon is a conversational AI platform company that empowers brands to deliver customer experiences through AI agents. This Senior Data Scientist role focuses on analyzing, testing, and applying data across the business to inform product strategy and company performance. Responsibilities include building datasets and metrics, enhancing data models, partnering with Engineering and Product on experimentation and measurement, building the data stack, and evaluating ML model performance.
ShipEngineeringSan Francisco, CAFeb 237