ML Researcher

4+ years of experience

Ukraine, Europe

Fulltime, remote

Our client is a leader in AI-powered performance marketing, operating across 25+ verticals with unmatched precision, speed, and scale. Their proprietary technology stack integrates seamlessly with major media platforms, enabling real-time event-level data exchange, optimization, and attribution.

At the core of their operation is a deep commitment to AI-driven decision-making. From real-time bidding engines and predictive lead scoring to campaign automation and anomaly detection, their in-house AI models are central to how they scale campaigns, reduce inefficiencies, and outperform market benchmarks.

They’ve built and continue to evolve a robust internal platform to empower media buyers, analysts, and operators with real-time alerts, smart recommendations, and semi-autonomous optimization tools.

We are looking for an experienced ML Researcher, whose role is to own the full lifecycle of machine learning projects -from problem formulation and research through production deployment and monitoring. You will design, build, and deploy ML models, mainly on tabular data, with full ownership over their production performance and business impact.

Responsibilities:

Develop, train, and evaluate machine learning models, with a primary focus on tabular, predictive, and ranking models.
Conduct feature engineering, data preprocessing, and dataset preparation in collaboration with data engineering teams.
Design, run, and analyze experiments to validate hypotheses and improve model performance.
Optimize models for production use, balancing accuracy, stability, and scalability.
Collaborate closely with data engineers to ensure reliable data pipelines and efficient model integration.
Contribute to production-first ML solutions across the marketing funnel, from training to deployment.
Participate in model validation, performance analysis, and continuous improvement cycles.
Support deployment and maintenance of ML models in production environments.

Requirements:

Strong hands on experience with ML models for tabular data and deep understanding of underlying methodologies.
Hands-on experience experience with end-to-end project ownership from research to production.
Proven ability to extract predictive signal from complex, messy real-world data at scale.
Experience training models on Big Data and optimizing for inference latency.
Experience with ML cloud-based platforms and MLOps tools and practices (experiment tracking, model versioning, deployment pipelines).
Strong proven Python skills and familiarity with ML packages for tabular data processing (scikit-learn, PyTorch, pandas, polars etc.).
Solid understanding of experimental design, causality and model validation.
Experience working closely with data engineering pipelines.
At least Upper-Intermediate English level.

Nice to Have / Advantages:

BA in statistics, ML, computer science or related fields.
Experience with causal inference methods, uplift modeling, A/B testing.
Familiarity with modern LLM APIs (OpenAI, Anthropic, Google).
Experience packaging models, building inference endpoints, and optimizing latency.
Exposure to drift detection, data quality checks, and performance monitoring.
Experience with containerization (Docker) and serving frameworks (FastAPI, Flask,
TorchServe, BentoML, etc.).