Audio Systems Engineer

Antare - Audio Systems Engineer (Embedded DSP / Audio ML) About Antare

Do you want to join the (very) early stages of a technology company that’s got huge ambition and vision, as well as the backing to make it happen? At Antare, we’re utilising our industry knowledge to create innovative products that we believe will disrupt the market. This is a complete greenfield opportunity to be part of a product roadmap from day one, the rest remains in stealth.

The team founded Antare in 2024 comprising product designers and engineers - some of whom have worked together for over 15 years. Together, the companies we have built have been collectively acquired for billions of dollars: we have a genuine level of success under our belts that you could help to contribute to. Today, we’re a small, hands‑on team spanning product design, finance, hardware, and software engineering.

This is a rare chance to join a startup at the very beginning - where your curiosity, ideas, and input will directly influence the product roadmap. You’ll work closely with a small, experienced team building and shipping real systems from day one.

You’ll have a high degree of ownership and autonomy, and you’ll collaborate across hardware, embedded, cloud, and machine learning. It’s a high‑trust, low‑ego environment where we move quickly, prototype often, and make pragmatic trade‑offs to get to production.

We also use modern tooling (including AI‑assisted workflows) to stay focused and iterate fast and you’ll help shape the engineering foundations as we scale.

As an Audio Systems Engineer, you’ll own the end‑to‑end audio subsystem for a compact, low‑power edge device and its supporting cloud pipelines. From real‑time capture and on‑device DSP through our cloud audio processing pipeline. You’ll deliver robust performance in noisy, unpredictable environments, balancing latency, compute, quality, and power.

This role spans embedded systems, signal processing, and applied ML. We don’t expect you to be world‑class in every area, we care most about a drive to create brilliant products that end users love, with strong technical judgement, and the ability to lead the subsystem end‑to‑end with support from the team.

Some of the technologies we use, and day‑to‑day tasks may include developing with:

• Audio processing DSP blocks such as filtering, AGC/DRC, noise suppression, VAD, and echo control

• Implementation of audio algorithms in resource constrained environments

• Audio codecs and streaming/recording pipelines (e.g., Opus/AAC; RTP/WebRTC‑like patterns)

• Python for analysis, evaluation tooling, and data exploration

• Cloud pipelines for audio post‑processing and ML integration (AWS or similar)

• Proven experience shipping embedded audio systems (or major audio subsystems) into production. (e.g. (embedded device, comms, consumer hardware, wearables, robotics, automotive, etc)

• Strong fundamentals in audio/DSP and hands‑on tuning experience (e.g. AGC/DRC, filters, noise suppression; AEC/beamforming are a plus).

• Strong coding skills in C/C++, and comfort working with real‑time and performance constraints.

• Strong problem‑solving skills and ability to work in a fast‑paced startup environment.

• Excellent communication and collaboration abilities. You can work effectively with hardware, embedded, and ML/cloud engineers.

• Practical experience using Python (or similar) for analysis, benchmarking, and evaluation tooling.

• Hardware‑adjacent experience (e.g., I2S, ADC/DAC, DMA, SPI/I2C/UART, oscilloscopes/logic analysers).

• Microphone/speaker component selection or acoustic/mechanical experience (placement constraints, enclosure effects, environmental robustness).

• Experience with audio measurement techniques and designing repeatable evaluation methodologies.

• Experience with always‑on / low‑power detection or audio classification (e.g., VAD, keyword spotting, event detection).

• Familiarity with ML frameworks (PyTorch/Tensor Flow) and the realities of deploying models into production pipelines.

• Experience with audio frameworks/libraries (GStreamer, WebRTC audio processing, Speex
  
  DSP, RNNoise‑like approaches).