Research Engineer - Robust Hashing & Representation Algorithms

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.

• 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.

• 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.

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)

• 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