AI Time-Series Data Synthesis Engineer

About the role

Responsibilities

• Design conditional time-series generation models preserving autocorrelation, seasonality, and multivariate co-movement
• Implement temporal fidelity evaluation metrics beyond distributional statistics: spectral similarity, autocorrelation distance
• Build domain adaptation enabling clients to specify target temporal properties for their synthesis use case
• Develop conditional generation conditioning on user-defined trend and regime change specifications
• Create multivariate synthesis preserving cross-series correlation structures
• Benchmark synthesis quality against domain-specific downstream task performance on synthetic versus real data

Requirements

• 3+ years in ML engineering with time-series modeling, generative models, or synthetic data experience
• Strong Python and PyTorch skills with sequence generation and temporal model architectures
• Deep understanding of time-series statistics: autocorrelation, spectral analysis, stationarity, and regime changes
• Experience evaluating generative model output quality beyond standard statistical distance metrics
• Ability to work across financial, sensor, and clinical time-series domains with different temporal fidelity requirements

Benefits

• Fully remote with flexible schedule
• Health and dental insurance
• $3,000 annual professional development budget
• 401(k) with match
• Equity options

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