Audio DSP Deployment Engineer

Hark is an artificial intelligence company building advanced, personalized intelligence. One that is proactive, multimodal, and capable of interacting with the world through speech, text, vision, and persistent memory.

We're pairing that intelligence with next-generation hardware to create a universal interface between humans and machines. While today's AI largely operates through chat boxes and decade-old devices, Hark is focused on what comes next: agentic systems that interact naturally with people and the real world.

To get there, we're developing multimodal models and next-generation AI hardware together - designed from the ground up as a single, unified interface for a new era of intelligent systems.

We're looking for an experienced Audio DSP Deployment Engineer to own how our audio algorithms work on device. Our algorithm team prototypes and validates new audio processing on the desktop in MaxMSP, JUCE, Python, and MATLAB, but the on-device implementation is its own design problem: what runs where, at what precision, in what block size, and within what compute, memory, and latency budget. You will own that design, reworking and extending algorithms so they hold up in real time on the embedded DSP cores inside our products, tuning them against real hardware and real acoustics, and carrying them through to a shipping feature. This is hands-on work close to silicon with genuine ownership of the on-device audio chain, and direct influence on how the product sounds and listens.

• →Own the on-device design of the audio algorithm chain: what runs where, at what precision and block size, and within what compute, memory, and latency budget
• →Take algorithms from desktop prototype in MaxMSP, JUCE, Python, and MATLAB to production C/C++ running in real time on the embedded DSP cores, re-architecting them where the target demands it rather than transcribing them
• →Develop, tune, and extend algorithms directly on device, and propose new processing where measurement or listening says the current approach falls short
• →Optimize for the target DSP architecture: vector and SIMD intrinsics, fixed-point versus floating-point trade-offs, memory placement and DMA, cache and tightly coupled memory behavior, block size and latency tuning
• →Profile and budget performance, measuring cycle load, memory footprint, and end-to-end latency, and removing hot spots
• →Validate against both the reference implementation and measured and perceived audio quality, and build the test fixtures and regression scripts that prove it
• →Debug on-device audio issues using platform tuning, logging, and real-time trace tools
• →Work with the algorithm, acoustics, and hardware teams to shape what the on-device chain should do, not only how it gets built

• Strong modern C/C++ for real-time, resource-constrained embedded systems
• Solid DSP fundamentals: filtering, FFT and overlap-add processing, sample rate conversion, fixed-point arithmetic, and numerical precision
• Ownership of on-device algorithm design decisions: restructuring processing to fit real-time constraints, choosing precision and topology, and knowing when to redesign rather than port
• Comfort working from measurements and listening as well as from a reference implementation
• Demonstrated experience porting reference algorithms from MATLAB, Python, or desktop C++ to an embedded DSP or SIMD target while meeting cycle and memory budgets
• Fluency in an embedded toolchain: cross-compilation, on-target debugging, and profiling
• Experience with embedded audio frameworks and real-time processing pipelines on an SoC platform
• Ability to work directly with algorithm authors, acoustics, and hardware teams

• Experience developing audio processing modules within a vendor DSP audio framework on a mobile or consumer SoC platform
• Familiarity with MaxMSP and/or JUCE as prototyping environments
• Audio domain depth in echo cancellation, noise suppression, beamforming with MEMS microphone arrays, loudspeaker protection, or spatial audio
• SIMD optimization on ARM NEON or comparable vector units, RTOS concepts, and audio HAL or driver-level integration
• Acoustic measurement literacy (Klippel, SoundCheck, APx) and experience building automated audio regression tests