Website:
qicap.ai
Job details:
About QiCAP.Ai
QiCAP.Ai is a quantitative investment and trading firm that captures opportunities across asset classes in global markets using high-frequency trading systems and strategies.
Our team of engineers, researchers and mathematicians from the top IITs and leading global institutions builds ultra-low-latency trading and simulation systems powered by advanced machine learning and quantitative models. Experienced traders and investors worldwide use these tools to trade smarter, manage risk more effectively and improve returns, even in unpredictable markets.
We love hard problems. Financial data is noisy, non-stationary and adversarial, which makes it one of the hardest places to make ML work. If you want to see your models tested against live markets, we'd like to hear from you.
What You'll Do
- Design, train and evaluate ML models (gradient-boosted trees, deep sequence models, transformers) on very large volumes of tick-level and order-book data, including online and adaptive approaches (bandits, reinforcement learning) for decisions that must adapt as markets shift.
- Tackle problems where signal is weak and dynamics are only partly observable: market behavioural patterns, cross-correlations across instruments, short-horizon return and volatility forecasting, fill and cancel probability, queue dynamics, and regime and change-point detection.
- Engineer features from raw market microstructure data and build the pipelines that serve them consistently in research and production.
- Make models production-ready under tight latency and memory budgets, using distillation, quantization, pruning and efficient inference.
- Own projects end to end, from hypothesis through modelling, backtesting and simulation to live deployment and drift monitoring, with rigorous validation throughout (walk-forward and purged cross-validation, leakage checks, calibration, honest out-of-sample measurement).
What You'll Get
- Direct exposure to production high-frequency trading infrastructure and live markets, where your model's value is measured in PnL, with access to state-of-the-art AI models and large-scale compute.
- Mentorship through code reviews and research discussions, in a collaborative, high-ownership environment where good ideas ship quickly, whoever they come from.
What We're Looking For
- Currently pursuing, or have completed, a B.Tech in Computer Science and Engineering, Electrical/Electronics Engineering, Data Science, or Mathematics and Computing, with 0-2 years of experience (internships and research work count) and a CGPA of 8.5/10 or above.
- Strong foundations in probability, statistics, linear algebra and optimization (convex optimization, estimation theory or information theory is a plus), and solid ML fundamentals: bias-variance, regularization, overfitting, cross-validation, loss design, and evaluation under class imbalance and distribution shift.
- Hands-on experience with deep learning in PyTorch or TensorFlow, and with classical ML (XGBoost, LightGBM, scikit-learn).
- Proficiency in Python and its scientific stack (NumPy, Pandas, SciPy), along with working knowledge of C++ fundamentals, since models and features must eventually run in low-latency systems.
- First-principles thinking, the ability to take on open-ended problems independently, and a bias for execution: you iterate quickly, measure results honestly, and report negative results as readily as positive ones.
Good to Have
- Time-series modelling (LSTM/TCN, transformers, HMMs, Kalman filters), including on noisy or non-stationary data.
- Reinforcement learning, bandits or online learning; probability calibration and change-point detection.
- Research publications or strong results in Kaggle, quant or ML competitions, or in competitive programming and maths.
- Interest in financial markets, market microstructure and trading.
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