CHAOS Inc. is looking to build AI for systems that detect and protect against next-generation aerial threats in near real-time, shipping models to the field rapidly to protect critical infrastructure and assets.
Requirements
- 8 - 15+ years of hands-on experience covering Data Science, data engineering, setting up model building and inferencing pipelines and using data science to solve real business and other problems.
- Experience building agents using contemporary frameworks (e.g. Langchain etc.)
- Experience with libraries like PyTorch, TensorFlow/TensorRT, streaming image classification or arbitrary or targeted shape detection in point clouds (Radar / Lidar) are going to be highly valuable
- Any experience with Analog or Digital RF signal processing will make you standout (although not required)
- Experience leading small teams of junior engineers or mentoring them is required
Responsibilities
- Drive the development, integration, and deployment of AI/ML-powered capabilities across CHAOS’ product lines.
- Ensure advanced AI research translates into tangible, real-world testable shippable software especially in defense applications.
- Deliver AI powered solutions including but not limited to creative use of AI and Machine Learning technologies in solving actual business problems.
- Quickly come up to speed browsing existing code and documentation.
- Be motivated to learn unfamiliar subjects and drive your own learning needs.
- Continuously keep up with industry trends.
- Ensure our AI models are robust, interpretable, testable, and performant under real-world conditions—especially in adversarial, low-power, GPS-Denied and/or edge-deployed environments.
Other
- Highly motivated and mission-oriented
- Must have excellent communication and be comfortable in presenting your ideas to various stakeholders including customers.
- Comfortable working with distributed teams, partners, customers and other stakeholders to learn the requirements, come up with a proposal for a solution, rapidly develop prototypes and finally perhaps implement and roll them out eventually to production with low oversight.
- People who never say, “not my problem”.
- On-site 2-3 days a week in San Francisco, CA
