DSP Firmware Engineer

Firmware Engineer Mid-level (L4 equivalent) 2-5 years

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The job description

Tech stack. Digital signal processing theory and practice, fixed-point arithmetic (Q-format), CMSIS-DSP library, FIR/IIR filter design, FFT implementations, audio and sensor pipelines, SIMD optimization (NEON/Helium), MATLAB/Python reference modeling

About the role

You will implement digital signal processing algorithms on embedded processors: the filters, transforms, and estimation algorithms that convert raw sensor data into meaningful, actionable information. DSP firmware engineers bridge abstract mathematics and concrete implementation, translating signal processing theory into efficient fixed-point code that executes deterministically in real time. You will optimize relentlessly for processor cycles and memory footprint, validate numerical behavior against floating-point reference models, and debug the subtle numerical issues that separate working DSP from silently broken DSP. Your code is what makes audio products sound excellent, sensor systems accurate, and motor controllers smooth.

What you will achieve

Implement efficient DSP algorithms including FIR/IIR filters, FFTs, and sensor fusion in fixed-point C, meeting hard real-time deadlines with measured timing margin
Deliver validated numerical correctness: bit-exact reproduction or rigorously bounded error relative to floating-point reference models
Optimize DSP processing pipelines for the specific target core: SIMD instruction exploitation, DMA-driven data flow, and cache-aware memory layouts
Debug signal processing defects systematically: spectral analysis of artifacts, numerical precision investigations, and fixed-point overflow diagnosis with proper tooling
Create reusable, well-tested DSP software libraries with clean APIs and documentation that accelerate all future signal processing work on the platform

What you will bring

Must-haves

2 to 5 years implementing DSP algorithms on embedded processors or dedicated DSPs in shipping commercial products
Strong DSP theoretical fundamentals: sampling theory, digital filter design, frequency-domain analysis, and fixed-point arithmetic error effects
Experience with CMSIS-DSP or equivalent optimized libraries, plus the ability to hand-optimize critical kernels when libraries prove insufficient
Proficiency in fixed-point implementation practice: Q-format selection methodology, scaling analysis, and systematic overflow prevention strategies
Real-time optimization capabilities: instruction cycle counting, SIMD intrinsic programming (NEON, Helium), and DMA-based data movement design
Rigorous validation methodology: comparing fixed-point implementations against MATLAB or Python references with explicitly defined error bounds
BS in Electrical Engineering or Computer Science with substantial DSP coursework

Nice-to-haves

Experience with audio DSP applications: codecs, acoustic echo cancellation, beamforming, or noise suppression systems
Familiarity with motor control DSP: field-oriented control implementations, state observers, or sensorless control algorithms
Knowledge of TinyML: deploying and optimizing machine learning models on microcontrollers
Experience with FPGA-accelerated DSP or heterogeneous multicore processing architectures