AI/ML Intern

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, 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

Job Type: Full-time

Work Location: In person