Weights & Biases Jobs
8 Weights & Biases jobs currently listed
Browse 8 open positions that require Weights & Biases 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
- 8 companies hiring
- Mostly Senior roles
- Often paired with Python, PyTorch, Docker, JAX
- Top locations: London, Munich, Bristol
Market insights
Autonomous Teaming is currently the most active employer hiring for Weights & Biases.
Seniority mix
- Senior 67%
- Mid 17%
- Lead 17%
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.
Machine Learning Engineer (m/f/d)
Darmstadt, Germany - English
ML Data Engineer (m/f/d) - Sensor Data & Pipelines
Munich, Germany - English (Fluent)
Senior GTM Systems AI and Automation Engineer
Remote (Worldwide) - English
Machine Learning Scientist — Large Multimodal Models (Post-Training)
Bristol, United Kingdom (Remote) - English
Lead Machine Learning Engineer - Geometric Spatial AI (F/M/D)
Munich, Germany - English (Fluent)
Frequently Asked Questions
- There are currently 8 open positions that list Weights & Biases 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 Weights & Biases 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.