Responsibilities
- Develop simulation and digital-twin tools that capture physical effects such as nonlinearities, noise, and precision limits, enabling hardware-aware algorithm development.
- Work closely with internal AI, software, and hardware teams to ensure tight alignment between algorithms, system constraints, and long-term platform goals such as automation of scientific workflows.
- Design and prototype machine-learning architectures and physics-aware optimization workflows for neuromorphic systems, exploring how AI models can be efficiently mapped onto emerging hardware substrates.
- Evaluate system-level performance tradeoffs, conduct benchmarking studies, document results, and contribute to technical decision-making across Axiomatic’s R&D portfolio.
Requirements
- PhD in Computer Science, Artificial Intelligence, Physics, Electrical Engineering, Applied Mathematics, or a related field.
- Strong background in machine learning and neural-network architectures, with experience or strong interest in hardware-algorithm co-design, neuromorphic systems, or analog/unconventional computing.
- Expert-level proficiency in Python and modern ML frameworks.
- Solid understanding of scientific computing, modeling, and data-driven experimentation.
- Ability to collaborate effectively in a multidisciplinary, fast-paced research environment.
Nice to Have
- Background in photonic, analog, or non-CMOS computing systems.
- Experience with spiking neural networks, energy-efficient AI, or hardware-aware ML.
- Familiarity with physical simulation tools or custom modeling pipelines.
- Contributions to open-source or research software projects.
Benefits
- Competitive compensation
- Stock Options Plan: Empowering you to share in our success and growth.
- Cutting-Edge Tools: Access to state-of-the-art tools and collaborative opportunities with leading experts in artificial intelligence, physics, hardware and electronic design automation.
- Professional Growth: Opportunities to attend industry conferences, present research findings, and engage with the global AI research community.
- Impact-Driven Culture: Join a passionate team focused on solving some of the most challenging problems at the intersection of AI and hardware.
Work Arrangement
Remote (Country) — Boston, US
Additional Information
- Team work model: Open to remote within the US
- Primary location: Boston, US
