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ML Researcher (Time Series / Signals) at ALT Fund. We are a prop-trading company that combines the agility of a startup with the resources of a high-performing fund. Our team is focused on developing cutting-edge strategies, and working with us means not just advancing technology, but also being part of a team where ideas are valued, professional growth is encouraged, and every member has the opportunity to unlock their full potential.. We’re looking for a Quantitative Researcher with a strong background in machine learning and time series modeling to join our team.. What You’ll Be Doing:. . Researching, developing, and deploying cutting-edge machine learning models for forecasting complex, high-dimensional time series — from market signals to macroeconomic indicators and alternative data.. . Building ML pipelines from scratch: data ingestion, feature processing, modeling, calibration, and monitoring.. . Designing custom validation and testing approaches for non-stationary data, including regime shift detection and adversarial evaluation.. . Working with large-scale data sources — tick-level, satellite, transactional, web-scraped — and transforming them into structured features.. . Collaborating with quants and engineers to integrate ML models into real-world investment processes.. . Contributing to strategic research initiatives, including causal inference, representation learning, and attention-based models for time series.. . Experience:. . 4–8 years of work experience, ideally a mix of academia and industry.. . Publications at top AI venues (NeurIPS, ICLR, ICML) in the fields of Time Series or Signal Learning.. . Experience building models that forecast market or alternative signals, macroeconomics, commodities, or sentiment.. . Participation in building an ML research culture: internal toolkits, mentorship, and open science practices.. . Skills & Education:. . Expertise in deep learning for time series: Temporal Fusion Transformers, DeepAR, N-BEATS, PatchTST.. . Knowledge of causal inference and counterfactual reasoning for time series.. . Experience in multi-modal learning (time series + tabular data + text).. . Proficiency with the ML stack: PyTorch, HuggingFace, DVC, Docker, etc.. . Skills in model validation for non-iid data: custom cross-validation strategies, regime-aware data splits.. . Ability to build end-to-end ML pipelines — from data ingestion to production inference.. . Master’s degree or PhD in a quantitative field (Physics, Mathematics, Computer Science, or related areas).. . Languages: Russian, English.. . Nice to have:. . Understanding of option pricing models, hedging.. . Experience with C++ or Rust.. . Ability to communicate technical ideas to diverse audiences, including non-technical stakeholders.. . Company Location: United Arab Emirates.