ML Researcher; NXJ

We are looking for a ML Researcher to design, train, and optimize tabular, predictive, and ranking models that directly impact performance and ROI. You will work end-to-end—from data exploration and feature engineering to validation and experimentation—bringing models into production in close collaboration with data engineering and ML teams. Your work will power real-time decisioning and optimization in an AI-driven performance marketing platform operating at scale.

• Develop, train, and evaluate ML models, with a focus on tabular, predictive, and ranking models
• Work with data across the full funnel to support performance and optimization use cases
• Contribute directly to production-focused modeling, ensuring models are practical, scalable, and reliable in real-world use

• Strong hands‑on experience with tabular ML models (XGBoost, Light
  
  GBM, Cat Boost, logistic regression, etc.)
• Proven experience with data wrangling, feature engineering, dataset preparation
• Experience training, tuning, and evaluating models at scale
• Solid understanding of model validation
• Strong Python skills and familiarity with ML libraries
• Ability to translate hypotheses into measurable experiments
• Experience working closely with data engineering pipelines

• Experience with inference engineering: packaging models, building inference endpoints, optimizing latency
• Exposure to model monitoring: drift, data quality checks, performance monitoring
• Experience with containerization (Docker) and serving frameworks (FastAPI, Flask, Torch Serve, Bento, etc.)
• Experience deploying ML models in production environments
• Prior experience with ranking, scoring or optimization models

• Competitive salary and benefits package
• Medical insurance
• Top equipment kit
• Full Remote
• Collaborative and innovative work environment
• Career growth and development opportunities
• A chance to work with a talented and driven team of professionals

An AI‑powered performance marketing company that manages and optimizes campaigns at scale across a broad range of verticals. The business is built around data‑driven decision‑making and automation, using a proprietary technology stack that connects with major advertising and tracking ecosystems to support real‑time optimization and reliable measurement. Their internal platform streamlines day‑to‑day operations for performance teams by providing centralized monitoring, fast feedback loops, and automated controls that reduce manual work.

In‑house machine learning supports smarter decisioning across core workflows—helping improve efficiency, maintain stable performance, and scale campaigns with consistency while staying focused on measurable business outcomes.