Develop and optimize GPU-accelerated simulation frameworks to test physics-based computing systems for machine learning. Collaborate with hardware and algorithm teams to integrate device models and ensure reproducible experiment tracking.
Unconventional, Inc.
8 Remote Job Openings at Unconventional, Inc.
Define and implement comprehensive verification plans and environments for IP blocks and full chips from specification to tape-out. Collaborate with cross-functional teams to debug complex hardware/software interactions and ensure 100% verification closure.
AI Systems, Language & Reasoning Models
Unconventional, Inc.
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Full Time
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3 hours ago
Unconventional, Inc.
Develop foundational language and reasoning models tailored for unconventional silicon to achieve extreme energy efficiency. Collaborate with hardware designers to co-design model architectures that leverage the physical dynamics of semiconductors.
Build and maintain optimized training stacks for generative vision, language, and world models. Design and scale multi-node distributed training systems and optimize low-level kernels to improve compute efficiency.
Drive the invention, prototyping, and validation of core components for a novel, energy-efficient computing platform. Work across theoretical modeling, algorithm development, and hardware/software co-design to map neural networks to device physics.
Develop physics-based system models and GPU-accelerated simulations to evaluate unconventional computing systems for ML workloads. Collaborate across teams to optimize the trade-offs between algorithms and hardware through high-fidelity differential equation solvers.
System Modeling (Performance Models)
Unconventional, Inc.
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Full Time
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3 hours ago
Unconventional, Inc.
Develop high-fidelity power, performance, and area estimation tools for novel AI acceleration architectures. Collaborate across teams to create comparative analyses and support high-level system design and hardware verification.
Develop high-performance PyTorch components to model complex, time-varying dynamic systems for next-generation AI architectures. Build simulation frameworks that enable rapid iteration across physics-based computing systems for machine learning workloads.
