ML Researcher (NXJ-129)

We’re looking for an experienced ML researcher 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 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

Requirements

• Bachelor’s degree in Machine Learning, Statistics, Computer Science, or a related field

• 3+ years of experience developing ML models for tabular data, with strong understanding of underlying methodologies

• Proven experience owning ML projects end-to-end — 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

• Hands-on experience with cloud-based ML platforms and MLOps practices (experiment tracking, model versioning, CI/CD, deployment pipelines)

• Strong Python skills and solid experience with tabular ML libraries (scikit-learn, PyTorch, pandas, polars, etc.)

• Solid understanding of experimental design, causality, and robust model validation techniques

• Experience working closely with data engineering pipelines

Will be a plus

• Advanced degree in Machine Learning, Statistics, Computer Science, or a related field

• Hands-on experience with causal inference, uplift modeling, A/B testing, and MAB

• Familiarity with modern AI APIs (OpenAI, Anthropic, Google), RAG pipelines, and/or agentic AI systems

• Experience deploying ML models to production: packaging, building inference APIs, and latency optimization

• Familiarity with drift detection, data quality checks, and model performance monitoring

• Experience with Docker and model-serving frameworks (FastAPI, Flask, TorchServe, BentoML)

What we offer

• 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 professional

About the project

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.