Contentful

Scaling

Enterprise · Headless CMS

Currently tracking 3 active AI roles, down 44% versus the prior 4 weeks. Primary focus: Agent · Engineering.

Hiring
3 / 3
Momentum (4w)
-18 -44%
23 opens last 4w · 41 prior 4w
Salary range
Tracked since
Jan 22
last role today
Hiring velocityscroll left for older weeks
2 new roles
Mar 25
1 new role
Apr 1
1 new role
Jul 22
1 new role
Feb 24
1 new role
Mar 10
1 new role
Aug 18
1 new role
Dec 8
4 new roles
Jan 5
1 new role
12
7 new roles
19
6 new roles
26
2 new roles
Feb 2
1 new role
9
14 new roles
16
4 new roles
23
3 new roles
Mar 2
3 new roles
9
22 new roles
16
3 new roles
23
7 new roles
30
9 new roles
Apr 6
4 new roles
13
2 new roles
20
8 new roles
27
9 new roles
May 4

Jobs (3)

3 AI · 113 total active
TitleStageFunctionLocationFirst seenAI score
Full Stack Engineer - Analytics (f/m/d)
Full Stack Engineer to build AI interfaces and dashboards on top of a data platform, working with AI agents and large-scale data pipelines. Focus on performant UIs and powering APIs.
AgentServeEngineeringBerlin, Germany2d ago7
Senior Machine Learning Engineer (f/m/d)
Senior Machine Learning Engineer at Contentful, focusing on building and optimizing generative AI solutions for enterprise customers. The role involves designing, building, and measuring production ML workloads, optimizing generative AI products for accuracy, speed, and scalability, integrating ML cloud solutions, and applying prompt engineering and fine-tuning. It requires technical leadership in product software development, experience deploying LLM-based models, understanding customer needs, and familiarity with distributed systems and cloud services. The role also emphasizes staying up-to-date on LLM developments like RAGs, open-source models, and agent architectures.
AgentPost-trainEngineeringLondon, United KingdomJan 227
Senior Machine Learning Engineer (f/m/d)
Senior Machine Learning Engineer at Contentful, focusing on building and optimizing generative AI solutions for enterprise customers. The role involves designing, building, and measuring production ML workloads, optimizing generative AI products for accuracy, speed, and scalability, integrating ML cloud solutions, and applying prompt engineering and fine-tuning. It requires technical leadership in product software development, experience deploying LLM-based models, understanding customer needs, and familiarity with distributed systems and cloud services. The role also emphasizes staying up-to-date on LLM developments like RAGs, open-source models, and agent architectures.
AgentPost-trainEngineeringLondon, United KingdomJan 227