Machine Learning Researcher
Job Details
About the Company
With operational hubs scattered across Europe, Asia, and LATAM, and its headquarters situated in San Francisco, US, the company boasts a workforce of over 1,000 adept professionals. Spanning across more than 20 countries, ALLSTARSIT offers a diverse range of skilled employees across various verticals, including AI, cybersecurity, healthcare, fintech, telecom, media, and so on.
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
Develops, trains, and evaluates ML models - especially tabular, predictive, and ranking models - and contributes directly to production-first modeling efforts across the funnel.
Specialization
Headquarters
Years on the market
Team size and structure
Current technology stack
Required skills:
- Strong hands-on experience with tabular ML models (XGBoost, LightGBM, CatBoost, 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
Preferred Qualifications (Nice to Have)
- 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, TorchServe, Bento, etc.)
- Experience deploying ML models in production environments
- Prior experience with ranking, scoring or optimization models
Scope of work:
Why ALLSTARSIT?
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