Audio Software Engineer
Audio Software Systems Engineer (Audio AI / ML / Embedded DSP )
About Antare
Join Antare as we go to market. We’ve been busy building the physical security intelligence platform of the future. Our always-connected platform automatically detects risks, captures evidence and reports on real-world events as they happen, delivering intelligent insights through an intuitive user interface, all hosted in the cloud. Our product is designed so commercial organisations - including, but not limited to, private security, retail, and hospitality - never miss an incident.
The Antare founding team of product designers and engineers have together built companies that 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 growing, hands-on team spanning product, design, finance, marketing, sales, hardware, and software engineering.
What we can offer you
This is a rare chance to join the early stages of a startup - where your curiosity, ideas, and input will directly influence the product roadmap. You’ll be working closely with a small, experienced engineering team, building real features in a modern frontend codebase from day one.
We care deeply about helping early-career engineers grow fast and with confidence. You’ll benefit from hands-on mentorship, regular feedback, and the chance to pair with engineers who’ve built products at scale. It’s a high-trust, low-ego environment designed to help you accelerate your skills while contributing meaningfully.
We’re using modern technology stacks across our infrastructure and frontend, and AI-assisted tooling to move fast and stay focused - and you’ll be an integral part of shaping both the user experience and the engineering foundations as we scale.
Role Overview
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)
What we’re looking for
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.
Bonus points if you have:
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/TensorFlow) and the realities of deploying models into production pipelines.
Experience with audio frameworks/libraries (GStreamer, WebRTC audio processing, SpeexDSP, RNNoise-like approaches).
Responsibilities
Design, implement, and tune a robust end-to-end audio pipeline that performs reliably in noisy real-world environments.
Implement and tune DSP components under tight resource constraints (e.g., filtering, AGC/DRC, noise suppression, VAD, echo control).
Define objective metrics and build repeatable evaluation and regression testing harnesses (lab + field) to measure and improve performance over time.
Debug audio issues across the full stack using instrumentation, logs, profiling, and (where helpful) lab equipment.
Work with cloud/ML engineers to enable audio-based intelligence via offline and online processing pipelines.
Collaborate with cross-functional teams to make pragmatic trade-offs across quality, latency, compute, power, and system complexity.
Contribute to a culture of continuous improvement: code review, documentation, engineering best practices, and design discussions.
Ideally, you’ll have experience with some of the technologies we use. That said, we don’t expect you to know everything already. We care most about strong fundamentals, good judgement, and a track record of shipping.
We believe that diverse perspectives make better products, and we strongly encourage people from underrepresented backgrounds in tech to apply. If you’re unsure whether you meet every requirement, please still reach out, we’d love to hear from you.
Why Antare
Join Antare in London and contribute to a team that’s creating the next generation of innovative products. We offer a competitive salary, equity options, and healthcare, along with the opportunity to build a product that makes a real impact.
While we offer some flexibility, we’re an in-person team (4 days per week) - at this stage of the company, we’ve found that collaboration and ideation happen faster and more naturally when we’re in the same room.