Associate Applied AI (Deep Learning/Time Series)

Location: New York, NY, United States; office location: New York

Base salary: $100,000–$2,000,000 annually, plus potential discretionary bonus.

Company

hirify.global is a global investment banking, securities, and investment management firm with an AI Research group focused on deep learning and quantitative finance.

What you will do

• Design, implement, train, and evaluate deep learning architectures for forecasting, representation learning, and generative modelling of financial time series.
• Build 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 GPU clusters and cloud infrastructure using DDP, FSDP, mixed precision, hyperparameter search, and experiment tracking; optimize inference with quantization, distillation, and ONNX deployment.
• Develop financial evaluation frameworks covering walk-forward and purged cross-validation, embargo periods, regime analysis, calibration, ablations, and significance testing.
• Partner with quantitative researchers and strategists on alpha research, signal generation, portfolio optimization, and backtesting, translating model outputs into risk-adjusted, capacity-aware signals.
• Contribute reusable models, datasets, benchmarks, and tooling to a firmwide research platform; mentor junior staff, review research and code, present findings, and maintain model governance, documentation, lineage, and auditability.

Requirements

• Bachelor’s, Master’s, or Ph.D. degree in Computer Science, Machine Learning, Statistics, Mathematics, Physics, Electrical Engineering, Quantitative Finance, or a related quantitative discipline.
• At least 3 years of industry experience building, training, and deploying deep learning models, with substantial sequential or time series experience; Ph.D. candidates with fewer industry years may qualify when research depth is demonstrably equivalent.
• Deep expertise in CNNs, TCNs, Transformers, autoencoders, GANs, diffusion models, GNNs, Bayesian methods, and reinforcement learning.
• Strong knowledge of ARIMA, GARCH, Kalman filtering, state space models, and judgment about when deep learning outperforms classical methods.
• Practical experience with WaveNet, N-BEATS/N-HiTS, DeepAR, PatchTST, or Time Series Foundation Models.
• Expert-level Python and deep proficiency in PyTorch, TensorFlow/Keras and/or JAX/Flax.
• Experience with DDP, FSDP, multi-node GPU training, and ONNX model export and optimization.
• Strong foundations in statistics, probability, stochastic processes, optimization, and signal processing.
• Excellent written and oral communication, ability to explain research trade-offs to Ph.D. researchers and non-technical stakeholders, and comfort working amid ambiguity and rapid change.

Nice to have

• Publications at NeurIPS, ICML, ICLR, AISTATS, KDD, or leading quantitative finance journals.
• Machine learning or data science experience at a hedge fund, asset manager, proprietary trading firm, or systematic trading desk.
• Experience across equities, fixed income, FX, commodities, or multi-asset classes.
• Familiarity with Bayesian deep learning, probabilistic programming, or conformal prediction.
• Experience with AWS, Kubernetes, Docker, or other cloud and containerized training environments.
• Exposure to model risk management or financial-services regulatory frameworks.
• Contributions to open-source machine learning or time series libraries.

Culture & Benefits

• Work on difficult research problems with measurable commercial impact across the firm.
• Access proprietary cross-asset financial datasets and GPU compute resources.
• Join a research group emphasizing strong baselines, rigorous evaluation, and reproducibility.
• Freedom to publish and engage with the research community.
• Direct collaboration with quantitative researchers and strategists across multiple desks.
• Opportunities to develop expertise at the intersection of deep learning and quantitative finance and influence the applied AI research agenda.