№ 1026 · DeFi and algorithmic trading · Hybrid · Moscow Archived
Deep Learning Researcher (Middle / Senior) for a product team at the intersection of DeFi, algorithmic trading and ML — hybrid in Moscow
A DL researcher with production time-series model experience for a small team working directly with the founder.
This job is closed. It stays here for reference — see the open jobs below or apply for another one. Open jobs →
About the company
A product team at the intersection of DeFi, algorithmic trading and ML. The team is developing a product in stablecoins and trading strategies: it works with market data, explores new approaches and deploys models that are used in the strategies.
This is not the team's first product: the previous one ran on real volumes and was profitable. The team is small, with no complex hierarchy, direct interaction with the founder and a tight link between Quant Research, ML and the engineering team.
About the role
A Deep Learning Researcher who has already built and launched production ML/DL models for time series and wants to apply this experience to tasks related to financial markets and trading strategies.
Experience in Quant/Trading is not required. What matters more is a strong ML/DL background, experience with time series, the ability to take models to production and an interest in financial markets.
What you will do
- Develop and deploy ML/DL models for time series forecasting
- Research architectures for forecasting and sequential modeling
- Work with historical and streaming data
- Experiment with features, architectures and approaches to model training
- Evaluate model robustness on non-stationary data
- Apply models in trading strategies together with the Quant Research team
- Analyse how models affect strategy results
What matters
- 2+ years of commercial experience as an ML Engineer / DL Engineer / ML Researcher / Data Scientist
- Experience developing production ML/DL models for time series
- Involvement in the full model cycle: data preparation, feature engineering, training, validation, deployment and analysis of results
- Confident Python
- Hands-on experience with PyTorch, Scikit-Learn, CatBoost, LightGBM/XGBoost, Optuna
- Understanding of time series, feature engineering and model quality evaluation
- A strong mathematical and technical foundation
- Russian for work communication; English for reading documentation and papers
Nice to have
- Experience with reinforcement learning
- Work with market or high-frequency data
- Experience with streaming, drift, model monitoring
- Kaggle, publications, competitions or research projects
- A personal interest in financial markets, trading or investing
- Experience in strong ML teams
Terms
- Hybrid format, office in central Moscow
- Fixed pay in stablecoins: USDT/USDC, 1:1 to the dollar
- Quarterly profit sharing based on strategy results
- Paid vacation and flexible sick leave
- The company pays for AI subscriptions and work tools