Hardware Platform Software Engineer

Great Sky•Boulder, CO

17d•$160,000 - $210,000•Hybrid

About The Position

Great Sky is a technology startup focused on rebuilding AI from first principles using neuroscience-inspired hardware and algorithms. The company is seeking a Hardware Platform Software Engineer to enhance its software platform for operating its hardware. This role involves developing a hardware abstraction layer (HAL) that translates abstract, model-level behaviors into concrete operations on analog hardware, and building an experiment framework for scientists and engineers to define and execute chip measurements efficiently. Ideal candidates will have experience owning substantial parts of hardware-facing software stacks for systems like quantum computers, analog accelerators, photonic processors, or large scientific systems.

Requirements

Bachelor's or Master's degree in Computer Science, Electrical Engineering, Physics, or a related field (or equivalent experience).
5+ years of professional software engineering experience for control of real physical hardware (e.g., quantum control stacks, novel accelerators, photonic systems, scientific instrumentation, embedded systems, or comparable platforms).
Demonstrated ownership of an API or abstraction layer used by other engineers, including designing interfaces, defending boundaries, and evolving them.
Superior proficiency in Python and working proficiency in at least one systems language (e.g., C, C++, or Rust).
Hands-on experience orchestrating lab instrumentation in software (e.g., SCPI instruments, DAQs, AWGs, cameras, motion stages, or cryogenic systems).
Strong testing instincts: unit, integration, and regression testing, hardware-in-the-loop test design, and simulation/hardware parity testing.
Excellent technical writing and communication skills, including high-quality architecture and interface documentation, and the ability to negotiate contracts across disciplines.

Nice To Haves

Experience with runtime or compiler-adjacent layers for novel accelerators.
Familiarity with superconducting electronics, photonics, or cryogenic systems.
Experience with streaming or high-throughput data systems (video, RF, DAQ).
Familiarity with neural networks and ML frameworks such as PyTorch or JAX.

Responsibilities

Build an enhanced hardware abstraction layer (HAL) that translates AI framework calls to driver-level calls for physical hardware, enabling model execution, capability discovery, versioning across hardware generations, and parameter translation.
Build a robust experiment framework for easy-to-use and elegantly declared measurement routines.
Provide analysis pipelines to capture maximum value from inferences and measurements.
Present a unified API for both simulated and physical backends, interfacing with the existing simulation framework for verification and parity testing.
Define and maintain the middleware between the driver layer and AI frameworks, introducing telemetry, diagnostics, and regression testing to ensure platform trustworthiness across hardware revisions.