Post-Graduate Quant Research Intern

Vega Solutions; Singapore; On-site; SGD 22,000 - 36,000; Part time

Job summary

Tokka Labs invites applications for a Graduate Quantitative Research Intern with a strong background in applied mathematics. You will tackle large-scale computational problems subject to low latency requirements, bridging the gap between optimization theory and high-performance execution.

Responsibilities include designing algorithms, providing rigorous analyses and proofs, building backtesting frameworks, and collaborating with teams to translate research into scalable, low-latency

Qualifications

• Academic background in quantitative fields is required.
• Strong grounding in numerical analysis, probability, and optimization.
• Experience coding algorithms in Python, C++, or Rust.
• Familiarity with optimization libraries or solvers is preferred.

Responsibilities

• Design, test, and refine pathfinding algorithms for speed and reliability.
• Provide rigorous analytical evaluations and proofs of convergence rates.
• Build backtesting frameworks and dashboards to compare methods with benchmarks.
• Collaborate with developers to translate research into low-latency implementations.
• Explore graph challenges to ensure system flexibility and robustness.
• Fulfil other research duties as assigned by the team.

Skills

Numerical analysis

Real analysis

Numerical optimization

Probability

Graph theory

Numerical linear algebra

Signal processing

Python

C++

Rust

Education

Masters or PhD in Applied Mathematics

Tools

SciPy

NetworkX

Gurobi

OR-Tools

Job description

About Tokka Labs

Tokka Labs is a proprietary trading firm with a focus on close collaboration, rigorous research, and cutting‑edge technology. We are market makers, searchers, and solvers for top protocols on the most popular blockchains in the world. We design and implement our own trading systems and strategies to provide liquidity in the most diverse and challenging environments. At the core of it all lies our unwavering commitment to pushing boundaries of decentralized finance and we are always on the lookout for like‑minded individuals to join us on this journey.

The Role

Tokka Labs is seeking a Graduate Quantitative Research Intern with a strong background in applied mathematics. In this role, you will tackle large scale computational problems subject to low latency requirements, bridging the gap between optimization theory and high-performance execution.

Responsibilities

• Algorithm Development: Design, test, and refine pathfinding algorithms to improve execution speed, routing reliability and overall solution quality.
• Mathematical Analysis: Provide rigorous analytical evaluations of optimization methods and models, including formal proofs of convergence rates and computational complexity.
• Benchmarking & Backtesting: Build backtesting frameworks and internal dashboards to compare proprietary methods against industry benchmarks.
• Production Integration: Collaborate directly with quantitative developers and systems engineers to translate research prototypes into low‑latency, scalable implementations.
• Advanced Research: Explore novel graph challenges (e.g. nonlinear and noisy edge costs) to ensure system flexibility and robustness.
• Ad-Hoc Initiatives: Fulfil other research duties and project responsibilities assigned by the team as needed.

Requirements

• Academic Background: Currently pursuing a Masters or PhD in Applied Mathematics, Electrical Engineering, Computer Science, Physics or a related quantitative field.
• Domain Expertise: Strong theoretical grounding in numerical analysis, real analysis, numerical optimization, probability, graph theory, numerical linear algebra, signal processing.
• Technical Proficiency: Hands‑on experience coding algorithms in Python, C++, or Rust, alongside familiarity with optimization libraries or solvers (e.g., SciPy, NetworkX, Gurobi, OR-Tools).
• Analytical Mindset: Proven ability to dissect complex theoretical problems, quantify time‑space complexity trade‑offs, and communicate technical concepts clearly.