Modeling & Simulation Engineer - Remote

About the role

This role involves building the simulation and modeling infrastructure for developing inference systems at Positron AI. 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 you'll do

• 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.
• 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, communicating assumptions and limits.
• Keep simulator interfaces aligned with production firmware, runtime, and host software.
• Validate numerical behavior and timing assumptions against reference implementations and available hardware results, building automated regression coverage.

What you'll need

• 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.
• Strong applied mathematics and quantitative reasoning skills.
• Ability to integrate complex software systems, debug interactions across components, and build maintainable tools.
• Clear technical writing and communication, including documenting assumptions, explaining discrepancies, and presenting architecture tradeoffs.

Nice to have

• Experience with one or more of: SystemC, Arm Fast Models (LISA), QEMU, gem5, or custom event-driven or cycle-level simulation frameworks.
• Experience modeling AI inference workloads, including tensor operations, quantization, memory traffic, and token-generation performance.
• Familiarity with embedded Linux, firmware or runtime integration, device models, PCIe, or direct memory access.
• Experience with performance traces and visualization tools such as Perfetto, model calibration, distributed simulation, or test orchestration at scale.
• Experience with functional programming in languages such as Haskell or Lean, and the ability to work with domain-specific command languages.

Skills: inference workloads, accelerator compute, memory, data movement, interconnects, simulation architecture, performance models