Systems Software Engineer (Rust, ML Inference)

Role Overview

ai-coustics is seeking a Systems Software Engineer to join our Systems team, working at the core of our real-time Audio AI SDK and inference infrastructure. In this role, you will help maintain, optimize, and expand the SDK that powers ai-coustics' speech enhancement and Voice AI products across a wide range of platforms, runtimes, and languages.

You will work primarily on our Rust-based inference and systems codebase, which underpins the Airten real-time inference engine, DSP modules, telemetry, model execution pipeline, and public SDKs used by developers worldwide. Your work will directly impact model performance, runtime efficiency, reliability, developer experience, and our ability to deploy neural audio models in latency-critical production environments.

This role sits at the intersection of systems programming, ML inference, real-time audio, and developer infrastructure. You do not need to be an ML researcher, but you should be excited about making neural networks run fast, safely, and predictably in real-world applications.

What You'll Do

• Design, implement, and optimize systems-level components of the ai-coustics SDK and inference runtime.
• Improve the performance, memory usage, and stability of the Airten real-time inference engine.
• Work on model execution, tensor operations, scheduling, streaming inference, and runtime abstractions.
• Support deployment of neural audio models across CPU, WASM, and other constrained runtime environments.
• Explore and integrate ideas from modern inference engines and ML runtimes such as Burn, ONNX Runtime, tract, TensorRT, or similar systems.
• Help bridge the gap between research models and production-ready, low-latency inference.

Audio, DSP & Real-Time ML Systems

• Develop and maintain DSP modules and supporting audio-processing infrastructure.
• Optimize streaming workloads under strict latency, jitter, and memory constraints.
• Build tooling to validate numerical correctness, real-time behavior, and model quality across platforms.
• Collaborate with ML researchers to make models easier to export, test, benchmark, and deploy.
• Contribute to model conversion and deployment workflows, including formats such as ONNX, internal model formats, or Rust-native representations.

Language Bindings & Platform Support

• Maintain and expand our C API and public C library generated from our internal Rust codebase.
• Improve and support SDK wrappers and bindings for C++, Python, and Rust.