Junior Machine Learning/AI Engineer at ramblr.ai

Gartenstadt, Bavaria, Germany -

Full Time

Start Date

Immediate

Expiry Date

06 Aug, 26

Salary

0.0

Posted On

08 May, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Multimodal Large Language Models, PyTorch, Computer Vision, Deep Learning, Transformer Architectures, CNNs, Linux, Git, Distributed Computing, Numpy, OpenCV, MLOps, CUDA, ONNX, Agentic Systems

Industry

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 strong Machine Learning/AI Engineer who can deploy and maintain state-of-the-art AI models, and embed them in systems combining them with MLLMs, computer vision and other components. You write clean, efficient and highly parallelized code to improve and extend our physical AI stack. You work on improving our platform to make building and deployment of features more efficient. By applying your understanding of state-of-the-art deep neural networks and modern agentic complex systems, you are responsible for the performance and reliability of our platform. You build and optimize parts of our AI stack and efficiently integrate them into our products. Your profile Passion for solving the hard problems in physical/visual AI Required: Full proficiency in python-based collaborative software engineering: follow consistent style-guide, clean design patterns, self-documented code, unit/integration tests, type annotations Multimodal Large Language Models: usage in production beyond prompt engineering, evaluation, integration into multicomponent/agentic systems Machine Learning: data loading pipelines, serving/deployment, neural network architectures (especially Transformer / CNNs), model optimization, PyTorch Experience with scientific python libraries such as: numpy, openCV General: git VCS, code reviews, development on Linux, distributed computing concepts Good mixture of leveraging modern coding assistants and excellent coding skills Optional: Computer Vision: CNNs, Vision Transformers, spatio-temporal data, image- and video embeddings, image augmentation Natural Language Processing: Transformer architectures, vision-language alignment, prompt engineering MLOps: automated model monitoring Acceleration in python: numba, c++/CUDA extensions, cython, PyTorch c++/CUDA extensions, ONNX, TPUs General: Cloud Technologies, Ray, data visualization Education M.Sc./Ph.D. in computer science, physics or mathematics with focus on computer vision, machine learning or a related field 1+ years of relevant work/internship experience Fluent in English Why us? Join a highly motivated team with super smart people in a well-funded, early-stage startup Take part in an incredible journey and participate in our equity incentive plan Become part of an international team of experienced entrepreneurs and deep-learning experts Play a decisive role in shaping a company with a creative working environment and streamlined decision-making Enjoy full responsibility for your tasks and your work area Come have fun with us, learn from your mistakes and bring good vibes! About us Founded by experienced tech entrepreneurs and deep-learning scientists with proven track records, we have embarked on a mission to bring AI to the physical world and unlock next-gen intelligent AR/XR devices.

Responsibilities

Deploy and maintain state-of-the-art AI models and integrate them into systems combining MLLMs and computer vision. Write efficient, parallelized code to optimize the physical AI stack and improve platform reliability.