Machine Learning Researcher

About the Project

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 we 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.

Role Summary:

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.

Required qualifications:
● 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

Preferred Qualifications:
● 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.