About the Role
We are building advanced algorithmic systems that require highly stable, noise-resilient, and transformation-robust representations. These systems must operate reliably even when inputs vary, compress, distort, or shift across different technical contexts.
We are looking for a Research Engineer with strong foundations in signal processing, hashing or encoding algorithms, mathematical modelling, and invariance design, and who is comfortable working with and evaluating modern large language models .
You will work at the intersection of algorithms, mathematics, and modern computational models, contributing to representation methods that must remain robust under a wide range of transformations.
The work is deeply technical, research- and delivery-driven, and highly applied — without being tied to any single domain.
What You Will Do
* Develop robust algorithms and representation methods that remain stable under transformations, noise, and perturbations.
* Design and analyse hashing, encoding, or similarity algorithms with strong invariance properties.
* Apply ideas from signal processing, information theory, and nonlinear transforms to real-world data.
* Evaluate behaviour of multiple LLMs (including Qwen-series models) under controlled variations or reparametrisations.
* Build experimental frameworks to test algorithmic stability, sensitivity, and discriminative power.
* Prototype new algorithmic approaches that generalise across diverse input forms.
* Work closely with engineers and researchers to integrate algorithmic insights into larger computational systems.
* Contribute to internal theory-building around representation robustness.
What You Bring
* Strong foundation in signal processing, transforms, hashing, encoding, or information theory .
* Ability to design or mathematically analyse novel algorithms beyond standard machine learning approaches.
* Experience with invariance, stability, perturbation analysis, or noise modelling.
* Solid mathematical background (linear algebra, spectral methods, applied maths).
* Comfortable running structured experiments with multiple LLMs (Qwen models especially welcome).
* Proficiency in Python (NumPy, SciPy, PyTorch/JAX optional but beneficial).
* Curiosity to explore new algorithmic directions and question assumptions.
* Desire to work on first-principles problems with real applied impact.
Ideal Backgrounds
This role suits outstanding early-career researchers such as:
* Engineers, PhD candidates or postdocs in:
* Signal Processing
* Applied Mathematics
* Information Theory
* Cryptography / Hashing Algorithms
* Electrical Engineering (DSP focus)
* Computational Physics
* Computer Science (algorithms, similarity, compression, security)
Personal Characteristics
* Analytical, rigorous, and detail-oriented
* Comfortable exploring abstract concepts and turning them into applied algorithms
* Approaches problems from first principles
* Enjoys working in a small, focused, research-heavy team
* Thrives in early-stage environments with high autonomy
* Motivated by solving challenging, foundational problems