Live opening · Posted 5 days ago
The key details from the original listing.
Live timing from JobBeeper.
Description supplied by the original job listing.
About ALP
ALP Tech (Angel Lane Partners LLC-FZ) is a quantitative finance, data, and AI consulting firm. We build and deliver platforms across risk, regulatory, and portfolio-optimisation domains for GCC financial institutions — banks, asset managers, and corporate treasury — and for UK clients, with coverage across the GCC and UK plus an India Delivery Centre in Bengaluru.
The role
We are hiring a PhD-level engineer to build and own the numerical compute systems inside ALP's fixed income, derivatives, and portfolio-optimisation platforms. This is a deep technical role for someone who models financial and mathematical processes and writes their own code — covering algorithms, implementation, correctness, and performance across C++, Python, and Rust. Specifics of the product area (e.g. XVA engines, portfolio optimisation, capital allocation) are shared during the interview process.
Responsibilities
Build production systems — we build quant models and solvers that power client platforms, not research prototypes
Implement numerical methods from research literature — fixed income, derivatives pricing, XVA, portfolio optimisation, capital allocation — into production code, verified against analytical solutions and reference data
Design and own performance-critical compute systems in modern C++ and Rust
Develop and optimise numerical solvers (e.g. convex and stochastic optimisation for portfolio construction), growing into performance-critical kernel-level work where needed
Build correctness and performance regression testing into CI
Write production-grade Python services (FastAPI/Flask) integrating quant models into client-facing platforms
Engage directly with GCC banking clients on requirements and validation
Raise the bar: testing discipline, code review, reproducibility
Requirements
PhD (or equivalent demonstrated depth) in Mathematics, Physics, Computational Science, Financial Engineering, or a related quantitative field
You have built numerical models or solvers yourself — not configured commercial tools — and can walk us through the methods, the code, and how you verified correctness (convergence behaviour, analytical solutions, reference data)
Strong programming ability across C++, Python, and Rust
Deep numerical instincts: discretisation, stability, convergence, floating-point error, optimisation
Comfortable working in the open-source/quantitative computing ecosystem — tools like QuantLib, Eigen, PETSc, IPOPT/CVXPY, NumPy/SciPy
Evidence of code you own: thesis solver, open-source contributions, publications with repositories
Willing and able to travel to the GCC approximately 30–50% of the time
Able to join within a 1-month notice period
Comfortable working in a startup/consulting environment with high ownership and agency
If you can pick up a quantitative finance or numerical methods paper, understand it, and implement it for a different problem from first principles — without vibe coding — we'd love to hear from you.
Nice to have
Direct exposure to fixed income, derivatives (XVA), or portfolio optimisation
Familiarity with GCC banking regulation — IFRS 9, Basel III/IV, RAROC, FTP
Exposure to LLM/AI integration — vector databases, RAG, embeddings (e.g. pgvector, LanceDB)
Prior experience delivering to GCC-region or financial-services clients
What we offer
Deep technical ownership on a small, senior engineering team with Masters and PhD in UK, India and Dubai
Direct client exposure across GCC banks and UK financial institutions
Competitive, experience-based compensation
Process (~2 days)
Intro call — 15–30 min
Two rounds of interview
Final conversation → offer
Reach out to alp.admin@alptech.io, with a relevant subject line and a short pitch about yourself.
Work arrangement
No
Recently tracked roles with full details and direct application links.