About Positron AI Positron AI specializes in developing custom hardware systems to accelerate AI inference. Backed by NEA.
About the role
Build the simulation and modeling infrastructure that helps Positron AI develop inference systems before silicon is available. You will develop analytical performance models, functional and timing simulators, and virtual platforms that support architecture exploration and realistic firmware, runtime, and host software workloads.
What they're looking for
- Strong software engineering skills in a systems language such as C++ or Rust, with experience building simulators, performance models, emulators, or comparable systems tools
- Deep understanding of processors, memory hierarchies, interconnects, concurrency, and the effects of data movement and contention on performance
- Experience designing and validating models with explicit goals for fidelity, determinism, and execution speed, and sound judgment about what to model or abstract
- Strong applied mathematics and quantitative reasoning skills, with the ability to turn workload behavior into useful models and experiments
- Ability to integrate complex software systems, debug interactions across components, and build maintainable tools that other engineers can use effectively
- Clear technical writing and communication, including documenting assumptions, explaining discrepancies, and presenting architecture tradeoffs
More about this role
Role Overview
Build the simulation and modeling infrastructure that helps Positron AI develop inference systems before silicon is available. You will develop analytical performance models, functional and timing simulators, and virtual platforms that support architecture exploration and realistic firmware, runtime, and host software workloads. The role combines computer architecture, software engineering, and quantitative analysis: choosing the right level of detail, validating model behavior, and making simulation a reliable part of everyday engineering.
Simulation Architecture and Modeling
- Develop analytical models of inference workloads to evaluate latency, throughput, utilization, and compute, memory, and communication bottlenecks.
- Build and extend functional and timing models of accelerator compute, memory, data movement, interconnects, and control processors, with explicit tradeoffs between model fidelity and simulation speed.
- Integrate accelerator models with virtual platforms and host interface models to support realistic software workloads and connected simulation environments.
- Compare current and proposed architectures through reproducible experiments, and...
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