Working Student - Machine Learning/Agentic Systems at Ramblr
Ramblr is hiring a Working Student - Machine Learning/Agentic Systems in Munich, Germany. On-site.
About Ramblr
Ramblr builds AI solutions for the physical world, focusing on egocentric data captured from a first-person view. The company provides a full-service egocentric data pipeline with tools for collecting, segmenting, annotating, and processing such data, as well as enterprise AI agents and data infrastructure for AI in production. Its applications include operator guidance for heavy machinery, industrial inspection, and surgical video analysis, alongside research projects in egocentric understanding and contextual AI.
Working Student - Machine Learning/Agentic Systems job description
ramblr
Actionable Insights with Industrial-Grade Video Understanding
AI for the Physical World. At Ramblr, we go beyond superficial video analysis to extract deep context from egocentric videos. Our technology provides a comprehensive understanding of actions, individual objects, and their relationships. Prompt Ramblr’s AI assistant to unlock precise insights and pinpoint specific moments in thousands of hours of multimodal videos captured from a first-person perspective, or find explanations and patterns in your videos you were not aware of.
Are you excited to become a Ramblr and join us at the intersection of AI and the physical world? If so, you can apply directly to the job posting or use the open application form.
We look forward to hearing from you !
Job Description
We are looking for a Working Student with a strong discovery and engineering mindset for AI and Machine Learning with experience in the usage of deep learning models as part of agentic/complex systems. You will contribute to the design, deployment, and improvement of physical AI systems leveraging multiple machine learning models in conjunction with (V)LLMs.
Your profile
- Profound knowledge in collaborative software development in Python: Follow consistent style-guide, clean design-patterns, write self-documented code
- Familiarity with multisensor datasets and scientific methods for their leverage
- Knowledge of agentic systems and their inner workings. Ideally, experience in their integration and design at multiple levels
- Machine Learning (ML):
- Ability to use PyTorch or Tensorflow, basics of neural network architectures (Transformer/CNN), model training
- Knowledge of classical ML methods: Unsupervised clustering, Gradient Boosting, SVM
- General: git VCS, code reviews, development on Linux, distributed computing concepts
Education:
- B.Sc./M.Sc. in Computer Science, Physics, Mathematics, Robotics or a related quantitative field.
Why us?
- Join a highly motivated team with super smart people in a well-funded, early-stage startup
- Take part in an incredible journey with very competitive salary
- Become part of an international crew of experienced entrepreneurs and AI luminaries
- Enjoy full responsibility for your tasks and your work area
- Come have fun with us, learn from your mistakes and bring good vibes!
