Offer summary

(Summary generated by AI based on the full job description)

The project focuses on internal initiatives in classical machine learning. The tech stack includes Python, pandas, NumPy, scikit-learn, Jupyter, SQL, XGBoost, LightGBM, MLflow, FastAPI, Git and Docker. Responsibilities cover preparing and validating datasets, exploratory data analysis, building and comparing models (regression, classification, clustering, dimensionality reduction, anomaly detection), feature engineering, hyperparameter tuning, maintaining reproducible ML pipelines and experiment tracking, exposing models via REST APIs or batch workflows, and writing tests and documentation. Offered resources include learning opportunities, internal conferences, medical insurance, sports activities and flexible work options.

from: 5 October 2026
to: 4 November 2026
salary not specifiedB2B contract (full-time)
salary not specifiedcontract of employment
Offer parameters
level:mid • senior
working mode:remote
Wrocław, Krzyki
Wrocław, KrzykiPowstańców Śląskich 7AView on map

Requirements

Expected technologies

Python
Pandas
SQL
Docker
Git
Pytest

Our requirements

  • 1+ year of hands-on Python development experience, including experience with pandas, NumPy, scikit-learn, and Jupyter;
  • A strong foundation in statistics, probability, and linear algebra, plus exploratory data analysis skills;
  • An understanding of supervised and unsupervised learning, and practical experience with regression, classification, clustering, dimensionality reduction, and anomaly detection;
  • Experience preparing structured datasets, including cleaning, transformation, encoding, scaling, and missing-value handling, plus practical feature engineering and feature-selection skills;
  • An understanding of train/validation/test splits, cross-validation, data leakage, overfitting, and regularization; the ability to select appropriate evaluation metrics, compare models, and perform error analysis;
  • Familiarity with hyperparameter tuning and reproducible ML pipelines;
  • SQL skills for data extraction and analysis;
  • The ability to expose models through REST APIs or batch-processing workflows;
  • Familiarity with Git, automated testing, Docker, and basic model monitoring;
  • The ability to explain model behavior, assumptions, limitations, and results;
  • English at B2 level or higher;

Optional

  • XGBoost, time-series analysis, recommendation systems, MLflow, model interpretability, cloud services, production ML monitoring, and GenAI, LLM, RAG, prompt-engineering, or agent-development experience;

Your responsibilities

  • Prepare and validate structured datasets for machine learning tasks, including cleaning, transformation, encoding, scaling, and missing-value handling;
  • Conduct exploratory data analysis to identify patterns, data quality issues, and opportunities for modeling;
  • Build, test, and compare ML models for tasks such as regression, classification, clustering, dimensionality reduction, and anomaly detection;
  • Develop and select features with guidance from senior engineers;
  • Run experiments, tune model parameters, and evaluate results using appropriate validation approaches and metrics;
  • Help maintain reproducible ML pipelines, notebooks, experiment tracking, and technical documentation;
  • Write and maintain automated tests for data and model-related code;
  • Support the delivery of models through REST APIs or batch-processing workflows;
  • Document model assumptions, results, limitations, and technical decisions;
  • Work closely with senior engineers, incorporate feedback, and gradually take ownership of more complex tasks;

About the project

You will work with senior engineers on internal classical machine learning initiatives. The role combines structured learning, practical delivery, and gradual ownership of increasingly complex tasks.
Technologies:
Python, pandas, NumPy, scikit-learn, Jupyter
SQL
XGBoost, LightGBM
MLflow
FastAPI
Git, Docker, pytest

This is how we organize our work

This is how we work

at the client's siteyou focus on a single project at a timeyou can change the projectyou have influence on the technological solutions appliedyou focus on product developmentagilescrum

This is how we work on a project

  • TDD
  • Continuous Deployment
  • Continuous Integration
  • DevOps
  • documentation
  • issue tracking tools
  • integration tests
  • performance tests
  • test automation
  • testing environments
  • unit tests
Company

What we offer

  • Resources and opportunities for self-education in technical and non-technical areas;
  • Access to internal conferences and meetups for knowledge sharing and learning from industry experts;
  • Medical insurance;
  • Sports activities to promote a healthy lifestyle;
  • Flexible work options, including remote and hybrid opportunities;
  • Referral program for bringing in new talent.

Development opportunities we offer

  • space for experimenting
  • substantive support from technological leaders
  • technical knowledge exchange within the company

COHERENT SOLUTIONS sp. z o.o.

Coherent Solutions is a digital product engineering company focused on empowering business success. Our global team of 2000 talented professionals collaborates seamlessly to deliver innovative solutions that drive measurable business impact. Headquartered in Minneapolis, USA, the company’s core competencies across 10 locations worldwide include software product development, IT consulting, data and analytics, machine learning, mobile app development, DevOps, Salesforce, and more.
We grow a team of advisors, not just order takers, and strive to make the company a place for career growth and opportunities. If you want to grow your core competencies, share your passion, and be sure every contribution is evaluated, we are on the same page.

This is how we work

ML Engineer
I apply to:
COHERENT SOLUTIONS sp. z o.o.
Wrocław, Krzyki
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