Graph Neural Networks Jobs
4 Graph Neural Networks jobs currently listed
Browse 4 open positions that require Graph Neural Networks experience. Every listing includes the full tech stack, language requirements, remote policy, and salary data, so you can filter with confidence instead of guessing from job descriptions.
By the numbers
- 3 companies hiring
- Mostly Principal roles
- Often paired with Python, GCP, PyTorch, AWS
- Top locations: London, Amsterdam
Market insights
Tripadvisor is currently the most active employer hiring for Graph Neural Networks.
Seniority mix
- Principal 50%
- Senior 25%
- Staff 25%
Top hiring companies
What you'll find here
Each job includes structured data from the original posting. You can see exactly which technologies are required vs. nice-to-have, whether the role is remote-friendly, and what salary range to expect, all without reading through pages of job descriptions.
Wise 1 month ago
Hybrid
Staff Applied ML Engineer - Financial Crime
London, United Kingdom - English
Python PyTorch Machine Learning
£145.000–182.000 /yearTripadvisor 2 months ago
Remote
Principal Machine Learning Scientist
London, United Kingdom (Remote) - English
Python TensorFlow PyTorch Spark Ray +2
Frequently Asked Questions
- There are currently 4 open positions that list Graph Neural Networks as a required skill. This number updates daily as new jobs are posted.
- We analyze every job description to identify the specific technologies mentioned. We distinguish between technologies that are required, nice-to-have, or explicitly not used, so you get an accurate picture of each role's tech stack.
- Yes. Jobdex supports negative filtering, which lets you exclude specific technologies from your search. For example, you can find Graph Neural Networks jobs that don't use a particular framework or language.
- When a company includes salary in their job posting, we use that directly. Otherwise, we parse salary information from the job description. Each salary figure gets a confidence score. High-confidence data is shown prominently, while less certain figures are clearly marked.