axon
AI Scientist I
At a Glance
- Location
- Seattle, Washington, United States
- Work Regime
- hybrid
- Experience
- 1+ years
- Posted
- 2026-05-21T18:08:37-04:00
Key Requirements
Required Skills
Domain Knowledge
- Embedded Systems
- IoT
- Robotics
Requirements
Strong proficiency in programming languages such as Python, C/C++, experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras and experience with ROS or robotic operational system.
Drive one or more phases of the ML development lifecycle: shape datasets, investigate modeling approaches and architectures, train/evaluate/tune models and implement the end-to-end training pipeline.
Leverage state-of-the-art research to deliver high quality models enabling multiple AI projects at scale.
Contribute back to the research community via academic publications, tech blogs, open-source code and contributing to internal/external AI challenges
Experience in developing computer vision algorithms for resource-constrained devices such as mobile phones, IoT devices, or embedded systems is highly desirable.
Compensation & Benefits
Competitive salary and 401k with employer match
Discretionary paid time off
Paid parental leave for all
Medical, Dental, Vision plans
Fitness Programs
Emotional & Mental Wellness support
Responsibilities
We are seeking a highly skilled and innovative Computer Vision and Machine Learning Scientist to join our AI team, focusing on AI applications in Cloud, Devices and Robotics.
As a key member of our research and development efforts, you will play a crucial role in advancing the state-of-the-art in Multimodal Large Language Models (MLLMs), Computer Vision technologies for our cloud, devices and robotics.
You will collaborate with cross-functional teams to design, develop, and deploy cutting-edge computer vision algorithms and solutions that enable intelligent perception and understanding of visual data.
Reports to: Senior Manager, Research Science
Research and develop advanced MLLMs, GenAI, and Computer Vision techniques for cloud, devices and sensors from multimodal data sources.
Design and implement efficient and scalable MLLM models for inference and analysis of visual data.