ML Researcher (NXJ-72)

Newxel; Poland; On-site; PLN 60,000 - 90,000; Full time

Benefits offered by this job

Competitive salary and benefits package

Medical insurance

Top equipment kit

Career growth and development opportunities

Job summary

Newxel is looking for an experienced ML researcher in Poland to own the full lifecycle of machine learning projects. You will develop, train, and evaluate ML models focused on tabular data, ensuring practical scalability and reliability in real-world applications. Key qualifications include strong Python skills, hands-on experience in model training, and proficiency with MLOps tools. The position offers a fully remote working environment, competitive salary, and benefits, along with collaboration in an innovative team.

Qualifications

• Strong hands-on experience with ML models for tabular data.
• Proven ability to extract predictive signal from complex data.
• Experience training models on Big Data and optimizing inference latency.

Responsibilities

• Develop, train, and evaluate ML models.
• Work with data to support performance and optimization use cases.
• Contribute to practical, scalable, and reliable modeling.

Skills

Machine Learning models for tabular data

Python and ML packages (scikit-learn, PyTorch, etc.)

Big Data training and inference optimization

Experiment tracking and MLOps

Education

BA in statistics, ML, computer science or related fields

Tools

Docker

FastAPI

Job description

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

• Strong hands on experience with ML models for tabular data and deep understanding of underlying methodologies
• Hands-on 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

Will be a plus

• 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.)

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.