Systems Software Engineer; Rust, ML Inference

Role Overview

the company 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 the company' 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.

Ideal starting date:
August/September

• Design, implement, and optimize systems‑level components of the the company 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

• 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

• 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 via the public C API
• Maintain WASM and Node.js SDKs built directly from the internal Rust source
• Ensure consistent behavior, performance, and API guarantees across Linux, macOS, Windows, WASM, and embedded‑adjacent environments

• Design, implement, and extend our testing pipeline, including unit tests, integration tests, numerical tests, and performance benchmarks
• Build tooling to validate real‑time constraints, memory usage, model outputs, and cross‑language consistency
• Improve CI workflows to ensure safe and fast iteration on a closed‑source core with public‑facing SDKs
• Create benchmarks and profiling workflows that help us understand runtime bottlenecks and performance regressions
• Improve observability and diagnostics for SDK integrations in customer environments

• Write and maintain technical documentation for SDK APIs, runtime internals, model deployment, and integration guides
• Collaborate with product and developer‑facing teams to improve onboarding and usability
• Support internal teams and external developers by diagnosing SDK and inference issues and proposing robust fixes
• Contribute to API design with a focus on ergonomics, safety, portability, and long‑term maintainability

• Strong experience in systems programming, ideally with Rust
• Solid understanding of C/C++ interoperability, ABIs, and FFI design
• Experience building or maintaining SDKs, libraries, inference runtimes, or developer‑facing systems
• Familiarity with real‑time systems, performance optimization, memory management, and profiling
• Experience writing tests and benchmarks for low‑level or…