AI/ML Intern

12 hours ago

Karachi, Sindh, Pakistan eSpark Talent Full time 500,000 - 1,500,000 per year

AI/ML Research Intern – Quant & Financial Intelligence

eSpark Talent
 is seeking exceptionally talented and highly driven individuals focused on quantitative finance, market prediction, and advanced machine learning. This role is designed for builders, problem-solvers, and researchers who want real-world exposure to financial AI systems and quantitative modelling. We want individuals who can think creatively, experiment rigorously, and build high-impact models.

Key Responsibilities:

  • Develop predictive ML models for equities, options, and market microstructure.
  • Engineer novel features such as volatility indicators, gamma exposure signals, and flow-based metrics.
  • Build and optimize ensemble architectures across classifiers, regressors, and time-series models.
  • Design backtesting and simulation frameworks for evaluating predictive signals.
  • Work with large-scale, noisy, real-time financial datasets.
  • Automate pipelines for data ingestion, cleaning, normalization, and labeling.
  • Experiment with feature extraction, including deep-learning-based embeddings.
  • Analyze model performance across various market regimes and conditions.
  • Present weekly research updates, insights, and model enhancements.
  • Prepare explainability reports (SHAP, ICE plots, feature attributions).

Requirements:

  • Demonstrated expertise through Kaggle competitions, notebooks, or structured ML pipelines.
  • Strong capability in cleaning, engineering, and synthesizing large, complex, and noisy datasets.
  • Experience with model stacking/ensembles, feature engineering, experimental design, and optimizing ML workflows.
  • Strong knowledge of Market prediction tasks like classification, regression, forecasting, and volatility modelling.
  • Concepts such as Options Greeks, GEX, volatility surfaces, order flow, and market microstructure (highly preferred).
  • Evaluation methodologies using financial metrics (Sharpe ratio, drawdown, expectancy).
  • Handling non-stationary data, regime shifts, and applying walk-forward testing.
  • Proficiency in Python (NumPy, Pandas, Scikit-Learn, PyTorch/TF/JAX).
  • Experience with experiment tracking tools (MLflow, Weights & Biases).
  • Ability to write clean, modular, research-grade code.
  • Capability to implement research papers and reproduce results from scratch.

Qualifications:
Bachelor's degree in CS, Data Science, Mathematics, Engineering, or a related field.

Preferred Skills:

  • Experience with deep learning for tabular or time-series models.
  • Knowledge of derivatives concepts (gamma, vanna, skew, etc.).
  • Prior exposure to quantitative research projects, university competitions, or academic papers.

Benefits:

  • Paid 3-month internship
     with hands-on quantitative research.
  • Direct mentorship from senior AI, ML, and quant research leaders.
  • Access to real, institutional-grade financial datasets.
  • Weekly research sprints and iterative project cycles.
  • State-of-the-art tooling, cloud access, and GPU compute resources.
  • Opportunity for a full-time role upon successful performance

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