[Nota AI GmbH] ML Researcher

The Research team is Nota AIs research group based in Europe focused on advancing model optimization for On-device AI and Physical AI.

Our goal is clear: to make Foundation Models such as VLMs and VLAs (Vision-Language-Action models) run in real time on actual devices and robotsnot just on servers. We pursue state-of-the-art performance with our research resulting not only in publications at leading conferences but also in technologies integrated into NetsPresso our On-device ML framework and ultimately into real-world products.

As a Machine Learning Researcher youll work closely with ML/DL Research Engineers Frameworks Engineers and Embedded Engineers taking part in the entire processfrom developing research ideas and publishing results to validating them on real hardware.

• Do both research and product. Your research wont stop at conference publications. Youll see your work deployed on real customer devices through NetsPresso.
• Research at the intersection of two rapidly evolving fields. Youll work where Efficient AI meets Physical AIan emerging research area focused on running Foundation Models in real time on robots and Edge devices.
• Small team big ownership. As an early member of our European entity youll have the opportunity to take ownership of the entire process from defining research directions to deployment while collaborating globally with our R&D teams in Korea.

1. Physical AI Research & Development

• Research robot Foundation Models based on VLMs and VLAs
• Fine-tune VLA policies and evaluate their performance through simulation and benchmarking
• Explore and apply the latest research in Physical AI including World Models and action-conditioned prediction

2. Model Optimization Research & Development

• Research optimization techniques for Foundation Models such as VLA VLM and LLM including Quantization (PTQ/QAT low-bit and mixed-precision) Pruning and Knowledge Distillation
• Research training-free and test-time optimization efficient attention and efficient inference techniques such as KV cache optimization and token reduction

3. Real-World Validation & NetsPresso Productization

• Profile latency and memory and validate deployment across diverse target hardware including GPUs NPUs robot arms and humanoid robots
• Evaluate the downstream task performance of optimized models and contribute new capabilities to NetsPresso

4. Research & Publication

• Publish research at top-tier conferences such as NeurIPS ICML ICLR CVPR CoRL and MLSys

• Ph.D. in Computer Science Engineering or a related field or equivalent research experience
• A strong passion for independently identifying research problems and seeing them through to rigorous validation
• Deep research or development experience in at least one of the following areas:
• Deep learning model optimization including Quantization Pruning and Distillation
• Training or fine-tuning Foundation Models such as VLMs VLAs and LLMs
• Professional experience with Python
• Strong proficiency in deep learning frameworks such as PyTorch
• Ability to investigate and analyze relevant prior research and independently reproduce published methods

• Publication record at top-tier conferences such as NeurIPS ICML ICLR CVPR ICCV/ECCV CoRL ICRA or MLSys.
• Experience deploying and evaluating VLA models or robot policies in simulation or on physical hardware.
• Experience in robot learning including imitation learning or reinforcement learning.
• Experience optimizing large-scale models such as LLMs or VLMs for model efficiency or inference/serving performance.
• Experience with On-device AI model conversion and deployment using TensorRT ONNX Runtime NPU SDKs or similar technologies.
• Experience developing Triton or CUDA kernels.
• Experience developing with the ML ecosystem including Hugging Face.
• Experience using AI coding tools such as Claude Code to automate research and experimentation workflows.

• Resume Review 1st Interview 2nd Interview & Online Coding Test 3rd Interview Offer Hire

Additional assignments may be included during the process.

AI is a team sport. Andrew Ng

We believe teamwork is essential to unlocking the full potential of AI research and development. When we work together we can achieve results that would be difficult to accomplish alone.

In our team we value every team members voice and contribution. We bring our strengths together challenge ideas openly and work collaboratively to turn ambitious research into meaningful innovation.

• This position is based at Nota AI GmbH in Berlin Germany.
• This job posting is open continuously and it may close early upon completion of the hiring process.
• Resumes that include sensitive personal information such as salary details may be excluded from the review process.
• Providing false information in the submitted materials may result in the cancellation of the application.
• Please be aware that references will be checked before finalizing the hiring decision.
• Compensation will be discussed separately upon successful completion of the final interview.
• There will be a probationary period after joining and there will be no discrimination in the treatment during this period.
• To support the employment of persons with disabilities you may optionally submit a copy of your disability registration certificate under Additional Documents if administrative verification is required. Submission is optional and does not affect the evaluation process.
• Veterans and individuals with disabilities will receive preferential treatment in accordance with relevant regulations.

Nota Life

• How We Put Our Core Values (CV) into Practice
• A New Hires Journey at Nota
• Take a Look Inside Our Office!

What We Do at Nota

• How Best to Explain What an AI Company Does
• A Look at NetsPresso Notas AI Model Optimization Platform
• A Look at Notas On-Device AI Solution
• Nota Tech Blog: Learn More