Systems Software Engineer (Rust, ML Inference)

ai-coustics is building the reliability layer for Voice AI the system that closes the gap between raw audio input and reliable machine understanding in production. By combining state-of-the-art speech and audio research with real-time production-grade SDKs we test observe and enable Voice AI systems to work in any environment. Our software is used by fast-growing Voice AI companies across Europe and the United States whose products require reliable performance at scale: call center agents voice agents telephony apps and enterprise voice assistants. We believe voice will become the main interface for technology and ai-coustics is building the foundational infrastructure to make audio input reliable measurable and easy to deploy.

We are backed by leading early-stage investors including Connect Ventures Partech Inovia Capital as well as angel investors from HuggingFace DeepMind and Amazon with deep expertise in AI and developer infrastructure. These partners share our vision and are helping us build a world-class team operating with high levels of responsibility and velocity. We look for people who take ownership think systemically and want to solve challenging real-world problems in close collaboration with our customers. If youre motivated by developing technology that is used in practice shaping an emerging category and setting a new standard for how voice AI works in the real world youll feel at home at ai-coustics.

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 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.

Ideal starting date: August/September

Tasks

ML Inference Engine & Runtime Development

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

Testing Reliability & Tooling

• 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

Documentation & Developer Experience

• 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

Requirements

Technical Skills

• 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 performance-critical code
• Comfortable working across multiple platforms such as Linux macOS Windows and WASM
• Ability to reason about API design unsafe boundaries ownership error handling and long-term maintainability

ML Inference & Audio Systems

• Familiarity with ML inference runtimes or deploying neural networks in production
• Experience with model formats or inference engines such as ONNX Burn tract TensorRT TFLite Core ML or similar systems
• Understanding of how neural networks are represented executed optimized and benchmarked
• Exposure to real-time audio constraints such as latency jitter buffering streaming workloads and deterministic processing
• Interest in making ML models portable efficient and reliable outside of Python research environments

Mindset & Collaboration

• Strong ownership mentality and attention to detail
• Comfortable working in a closed-source core with open SDK surfaces
• Ability to reason about trade-offs between performance safety portability and developer experience
• Clear written communication skills for documentation and technical design discussions
• Enjoys working in a fast-moving startup environment with real-world production impact
• Excited about building infrastructure that helps Voice AI systems work reliably in messy real-world audio conditions

Benefits

• Opportunity to work at a rapidly growing Voice AI startup backed by top investors.
• Compensation and equity: Competitive salary package additional benefits and stock options enabling you to take part in the companys success.
• Startup Culture: Dynamic fast-paced environment with passionate and collaborative colleagues.
• High Impact: Groundbreaking startup at a pivotal growth stage making a real difference in how people experience audio.
• Ownership & Autonomy: Take full ownership of projects and ship fast.
• Work With the Best: World-class team of engineers and builders with ample room for professional growth.
• Contribute to the Future: Define the landscape of Voice AI technology.

If you are ready to lead the charge in revolutionizing Voice AI and drive our startup to new heights we would love to hear from you. Apply today to join the ai-coustics team!