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.

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

Requirements

What You’ll Need

• B.Sc., M.Sc. or PhD in Computer Science, Applied Mathematics, Applied Physics, Control Theory, Electrical Engineering, or a closely related technical field
• Strong foundation in control theory and digital signal processing, including closed-loop design, stability and phase margin, discrete-time filter design, demodulation, I/Q processing, and phase-locked loops
• Practical experience with state estimation and Kalman filtering, including linear, extended, or adaptive approaches applied to real physical systems
• Strong Python or MATLAB skills for mathematical modelling, simulation, data analysis, and algorithm development
• Ability to translate complex physical behaviour into clear mathematical models and scalable algorithmic solutions
• Confidence working from first principles, including reading technical literature, extracting the underlying model, challenging assumptions, and turning theory into a working simulation
• Ability to work effectively across disciplines and communicate complex mathematical concepts to engineers working on hardware, FPGA, embedded software, and system-level development
• Experience writing maintainable C/C++ for embedded targets is strongly preferred
• Previous experience with resonant sensors, MEMS inertial sensors, vibratory gyroscopes, or similar sensing technologies is a strong advantage
• Familiarity with inertial navigation, sensor fusion, or GNSS-denied navigation is an advantage
• Experience within aerospace, defence, robotics, or another high-performance engineering environment is a plus
• Bare-metal or RTOS embedded development experience is a plus

Who You Are

You enjoy problems where physics, mathematics, and real hardware meet. You are analytical without getting stuck in theory and comfortable going from a research paper or physical model to simulation, implementation, and experimental validation. When measurements do not match expectations, you want to understand why. You take ownership of the technical answer, challenge assumptions with data, and work naturally with specialists across hardware and software to turn complex sensor behaviour into reliable, high-performance algorithms.