VP Applied AI - Deep Learning

Location: New York, NY, United States; onsite role.

Salary: USD 130,000–260,000 base salary. A separate source line states an expected base salary of $115,000–$300,000; discretionary bonus may also be available.

Company

hirify.global is a global investment banking, securities, and investment management firm headquartered in New York, with offices worldwide.

What you will do

• Conduct original research in deep learning, representation learning, time-series forecasting, generative modeling, and quantitative finance.
• Develop Transformer-based and time-series foundation models using large-scale pre-training, self-supervised learning, transfer learning, and adaptation across assets, market regimes, and forecasting horizons.
• Design rigorous evaluations covering predictive performance, robustness, calibration, economic relevance, walk-forward testing, leakage controls, regime analysis, and statistical validation.
• Partner with quantitative researchers, strategists, traders, and engineers on alpha generation, return and volatility forecasting, liquidity, market microstructure, execution, and portfolio construction.
• Build reusable research assets including pretrained models, datasets, benchmarks, evaluation protocols, and documentation.
• Mentor junior researchers and engineers, review research design and code, communicate findings to senior stakeholders, and follow model-risk, data-governance, privacy, ethics, and responsible-AI standards.

Requirements

• Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, Physics, Electrical Engineering, or a related quantitative discipline.
• At least five years of professional experience in machine learning, quantitative research, applied research, data science, or a closely related field.
• Strong research background in deep learning and representation learning, time-series forecasting and sequential modeling, quantitative finance, or market microstructure.
• Ability to formulate and independently execute rigorous research using large, complex, and high-dimensional datasets.
• Ability to communicate complex research ideas, empirical findings, and methodological trade-offs clearly and effectively.
• Ability to work independently on open-ended research questions while collaborating effectively with traders and engineers.

Nice to have

• Experience with Transformer-based models, probabilistic deep learning, or other modern sequence architectures.
• Experience with multivariate forecasting, cross-sectional prediction, or high-frequency market data.
• Research experience in quantitative trading, systematic investing, market microstructure, electronic execution, market making, statistical arbitrage, or financial econometrics.
• Publications or demonstrated research contributions in leading machine-learning, artificial intelligence, statistics, optimization, or quantitative-finance venues.
• Ability to connect methodological advances in AI with robust and economically meaningful results in financial markets.

Culture & Benefits

• Work on frontier financial-AI research involving deep learning, Transformer-based modeling, time-series foundation models, and quantitative finance.
• Access proprietary cross-asset market data and computational resources for large-scale model training and empirical research.
• Join a research culture focused on scientific rigor, strong baselines, robust out-of-sample evaluation, reproducibility, and economically meaningful results.
• Collaborate with quantitative researchers, strategists, and trading stakeholders across business areas.
• Participate in internal research forums and, subject to firm policies and approvals, external publications and engagement with the AI and quantitative research community.
• Access training and development opportunities, firmwide networks, benefits, wellness, personal finance offerings, and mindfulness programs.