About Us
Axiomatic_AI is dedicated to accelerating R&D by developing the next generation of Automated Interpretable Reasoning, a verifiably truthful AI model built for reasoning in science and engineering, empowering engineers specifically in hardware design and Electronic Design Automation (EDA), with a mission to revolutionize the fields of hardware design and simulation in the photonics and semiconductor industry. We seek highly motivated professionals to help us bring these innovations to life, driving the evolution from development to commercial product.
Position overview
As an AI Research Scientist with focus on Formal Verification for Physics, you will be part of a focused team working on the formal verification for physics initiative. You will analyze scientific challenges, design and develop experimental prototypes, and architect solutions while evaluating different approaches. Your role includes setting up machine learning experiments, running benchmarks, and proposing, debating, and implementing technical strategies. Staying current with cutting-edge methodologies is essential. You will work closely and directly with our interdisciplinary research team and help cultivate a collaborative and knowledge-sharing environment.
Your mission
- Formal Verification & Scientific Prototyping: Drive the development of AI techniques for the formal verification for physics initiative and develop exploratory prototypes to tackle scientific challenges.
- Architecture & Technical Solutions: Design robust system architectures, evaluate alternatives, and implement effective technical solutions.
- Experiments & Benchmarking: Set up Machine Learning experiments, run benchmarks, and analyze results to guide development.
- Collaboration & Teamwork: Work closely with Physicists and AI Researchers, fostering open collaboration and knowledge sharing.
- Continuous Learning & Innovation: Stay at the forefront of research, embracing new technologies and advancing your expertise.
Key requirements:
- PhD (or equivalent proven experience) in Computer Science, Artificial Intelligence, Physics, Machine Learning for Scientific Applications, or a related field.
- 1-2 years of hands-on experience in applying Machine Learning techniques to scientific or technical challenges.
- Strong communication skills and ability to collaborate effectively within a multidisciplinary and multicultural environment
- Proactivity, self-motivation, and commitment to continuous learning
- A collaborative team player with a curious, solution-oriented mindset
- Adaptability and capability of working in dynamic, fast-changing environments.