Modeling & Simulation Engineer

About Positron AI

Positron AI specializes in developing custom hardware systems to accelerate AI inference. These inference systems offer significant performance and efficiency gains over traditional GPU-based systems, delivering advantages in both performance per dollar and performance per watt. Positron exists to create the world's best AI inference systems.

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.

Key Responsibilities

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 communicate the assumptions and limits behind each result.

Production Alignment and Engineering Infrastructure

- Keep simulator interfaces aligned with production firmware, runtime, and host software so workloads, diagnostics, and tests can carry forward to hardware.

- Validate numerical behavior and timing assumptions against reference implementations and available hardware results; build automated regression coverage that distinguishes functional correctness from performance accuracy.

- Improve simulator speed and usability through configuration tools, checkpointing, deterministic execution, tracing, observability, and test automation.

Cross-Functional Collaboration

- Collaborate closely with hardware and software architects on command semantics, scheduling assumptions, and model interfaces, keeping simulator behavior consistent with architectural specifications and execution software.

- Partner with compiler, firmware, runtime, performance, and test engineers to turn models into practical development and qualification environments.

Required Qualifications

- 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.

Preferred Qualifications

- 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,