VP AI Research/Applied AI - Deep Learning & Time Series

Location: New York, NY, United States; office-based role

Base salary: $150,000–$300,000 annually, plus potential discretionary bonus.

Company

hirify.global is a global investment banking, securities, and investment management firm with engineering and AI research organizations.

What you will do

• Design, implement, and train deep learning architectures for forecasting, representation learning, and generative modelling of financial time series.
• Develop and benchmark specialized time series models, including WaveNet, N-BEATS, N-HiTS, DeepAR, PatchTST, and time series foundation models, against classical econometric baselines.
• Run distributed training across multi-node GPU clusters and cloud environments, including DDP, FSDP, mixed precision, hyperparameter search, and experiment tracking.
• Optimize inference using quantization, distillation, and ONNX deployment workflows.
• Build robust financial model evaluation frameworks covering walk-forward and purged cross-validation, regime analysis, uncertainty calibration, ablations, and significance testing.
• Partner with quantitative researchers, strategists, and engineering teams on alpha research, signal generation, portfolio optimization, backtesting, and production deployment.

Requirements

• Bachelor’s, Master’s, or Ph.D. degree in a quantitative discipline such as computer science, machine learning, statistics, mathematics, physics, electrical engineering, or quantitative finance.
• At least 7 years of industry experience building, training, and deploying deep learning models, including substantial sequential or time series experience; equivalent research depth may be considered for Ph.D. candidates.
• Deep expertise in modern deep learning architectures, classical time series and econometric modelling, and specialized time series architectures.
• Expert-level Python and deep proficiency in PyTorch, TensorFlow/Keras, and/or JAX/Flax.
• Experience with distributed and accelerated training, multi-node GPU environments, ONNX, and model optimization workflows.
• Strong foundations in statistics, probability, stochastic processes, optimization, and signal processing, with excellent communication skills.

Nice to have

• Publications at leading machine learning or quantitative finance venues.
• Machine learning or data science experience in hedge funds, asset management, proprietary trading, or systematic trading.
• Experience across multiple asset classes and familiarity with Bayesian deep learning, probabilistic programming, or conformal prediction.
• Experience with AWS, Kubernetes, Docker, model risk management, or open-source machine learning and time series libraries.

Culture & Benefits

• Work on difficult research problems with measurable commercial impact across the firm.
• Access proprietary cross-asset financial datasets and substantial GPU computing resources.
• Join a research group focused on rigorous baselines, evaluation, and reproducibility.
• Engage with the broader research community and publish research.
• Partner directly with quantitative researchers and strategists across multiple desks.
• Access training and development opportunities, firmwide networks, wellness programs, and personal finance offerings.