We’re building an AI-native quantitative trading platform — not a toy backtester, not another indicator factory. Our goal is to let traders and researchers create full-stack algos end-to-end: alpha generation, backtesting, optimization, and execution — using raw data and AI-assisted tooling.
This role sits between quant research, systems engineering, and applied AI.
What You’ll Do
You will help build internal tools and infrastructure that power systematic trading and AI-assisted research.
Core Responsibilities
Build quant research tools using Python/C++ (data pipelines, factor engines, strategy evaluation).
Develop AI-assisted workflows (LLM-driven feature generation, strategy templating, analysis summaries).
Work with raw market data (intraday OHLCV, tick, derived features).
Integrate AI into:
Alpha discovery
Pattern detection
Strategy diagnostics
Help design modular systems that allow fast experimentation (not hard-coded strategies).
Write clean, testable, production-grade research code.
Collaborate closely with senior quants and system architects.
This is a build-first role. You will ship tools weekly, not sit in meetings.
What You Need (Non-Negotiable)
Strong Python fundamentals (pandas, numpy, data structures).
Solid software engineering mindset (modular code, version control, debugging).
Comfort working with time-series data.
Familiarity with backtesting concepts (look-ahead bias, slippage, overfitting).
Curiosity about how AI can augment research, not replace thinking.
Ability to read vague problem statements and turn them into working systems.
Nice to Have (But Not Required)
C++ or Rust experience.
Experience with:
Market microstructure
Systematic trading strategies
Intraday / high-frequency data
Prior work using LLM APIs (OpenAI, Anthropic, etc.).
Familiarity with ClickHouse / PostgreSQL / Parquet.
Experience building internal tools rather than end-user apps.
What This Role Is NOT
* Not a signal-copying role
* Not manual trading
* Not a “prompt engineer” job
* Not a pure research or pure ML role
This is systems + quant + applied AI.
What You’ll Learn (Fast)
How real quant systems are built (not academic versions).
How to structure research for scale and reuse.
How AI actually helps discovery (and where it doesn’t).
How profitable strategies are engineered, tested, and killed.
If you perform, your scope will expand rapidly.
Compensation
Competitive junior-level base
Performance-based bonuses
Meaningful upside as responsibility grows
Who This Is Perfect For
Strong junior engineer who wants exposure to real trading systems
Someone bored of CRUD apps and dashboard work
A builder who learns fast and asks hard questions
If you’re looking for hand-holding, this is not it. If you want to build the engine, not click buttons, this fits.
Kindly note that only shortlisted candidates will be notified.
Related Job Searches:
- Company:
Varsity Holdings - Designation:
Junior Quant Systems Engineer (AI-native Trading Tools) - Profession:
Engineering - Industry:
Finance