ML Middle/Senior Engineer (Trading)

We are looking for a Machine Learning Researcher to design, develop, and evaluate predictive models for financial markets. You will work at the intersection of quantitative research, machine learning, and real-world trading constraints, contributing to alpha generation and risk modeling.Requirements:3+ years of relevant experienceStrong Python skills and experience with ML ecosystems (AWS Sagemaker, MLFlow)Hands-on experience working with tabular/time series data with usage of ML Solid understanding of machine learning fundamentals: Supervised learning, feature engineering, model evaluation; Overfitting, regularization, cross-validationKnowledge of statistical methods and probability theoryExperience with experiment design and offline evaluationAbility to work with large datasets and build efficient data processing pipelinesFamiliarity with SQL and data queryingStrong analytical and problem-solving mindsetAbility to clearly communicate findings and trade-offsOwnership of tasks from research to implementationCuriosity and willingness to explore new approachesLevel of English enough for efficient technical and business communication with native speakersNice to have:Experience in financial machine learning, quantitative finance, or trading systemsknowledge of signal generation, alpha research, portfolio construction or risk modelingExperience with: Deep learning for tabular/time series data (Transformers, RNNs, etc.); Probabilistic modeling or Bayesian methodsHands-on experience with production ML systems (MLOps, monitoring, retraining)Ability to define research direction and identify high-impact opportunitiesDecision-making under uncertaintyAbility to translate business problems into ML solutionsResponsibilities:Develop and validate machine learning models for financial time series and cross-sectional dataConduct research on alpha signals, feature engineering, and predictive modelling techniquesDesign experiments and backtesting frameworks with proper statistical rigorWork with large-scale structured and unstructured financial datasetsCollaborate with engineering teams to deploy models into production pipelinesAnalyze model performance, stability, and robustness under changing market conditionsImprove data pipelines, labeling strategies, and evaluation methodologiesWe offer:Projects for such clients as PayPal, Wargaming, Xerox, Philips, Adidas and ToyotaCompetitive compensation that depends on your qualification and skillsCareer development system with clear skill qualificationsFlexible working hours aligned to your scheduleOptions to work remotely