Digital Signal Processing Engineer

About the Role

Imagine this. You are working on a precision inertial sensor built around a mechanically resonant sensing element, where measurement quality depends on keeping that element oscillating at exactly the right frequency and amplitude and extracting a clean, reliable signal from it.

As a Digital Signal Processing Engineer, you will own the algorithms that make this possible. You will define how the control loops behave, build the estimation and compensation methods behind the sensor output, and establish the mathematical models used to understand and validate performance. Other engineers will own the electronics and RTL implementation. You will own whether the maths, algorithms, and control strategy are right.

At Destinus, we are revolutionizing the defense industry with cutting-edge Unmanned Aerial Vehicles (UAVs). Our innovative technologies are designed to meet the unique demands of modern defense operations, delivering unparalleled speed, precision, and cost effectiveness. Destinus partners with government agencies and defense organizations worldwide to provide advanced solutions for mission-critical operations, enabling a new era of efficiency and technological superiority. Join us in shaping the future of defense with groundbreaking aerospace innovations.

What You’ll Do

• Design and simulate the control loops that keep the sensing element stable and locked, including frequency tracking through PLL, amplitude control through AGC, and quadrature nulling
• Define loop architectures, bandwidths, gains, stability margins, and other control parameters based on sensor physics and system-level performance requirements
• Develop the slower outer-loop algorithms responsible for bias estimation, thermal compensation, and state estimation, ideally implementing them in maintainable C/C++ for embedded targets
• Design and implement Kalman filtering approaches that transform the output of the locked sensing element into an accurate and usable rate measurement
• Own the co-simulation environment used to compare hardware implementation against the physics-based reference model and identify the source of discrepancies
• Build mathematical and simulation models in Python or MATLAB to explore sensor behaviour, test algorithms, and validate design decisions before implementation
• Establish and interpret sensor characterisation methods including Allan variance, bias instability, angular random walk, scale-factor linearity, and related performance metrics
• Work closely with electronics, FPGA, embedded software, physics, and systems engineers to translate mathematical models into robust real-world implementations
• Investigate unexpected sensor behaviour from first principles, connecting physical effects, measurements, models, and algorithms to find the underlying cause