Machine Learning Engineer

Infinity Technologies AG; United States; Remote; USD 110,000 - 160,000; Full time

Job summary

Infinity Technologies AG is seeking a Machine Learning Engineer to build demand forecasting and personalization models for a subscription-based digital product. You will work with Python, PyTorch or scikit-learn, Pandas, SQL, Airflow, MLflow, Docker, and cloud deployments.

You will collaborate with international product and engineering teams, with strong English communication. The role emphasizes production-ready ML pipelines and measurable business impact.

Qualifications

• 3+ years of commercial experience in machine learning or applied data science.
• Strong Python skills and practical experience with Pandas, NumPy, scikit-learn, and/or PyTorch.
• Experience with SQL and working with production datasets.
• Experience with MLflow, Airflow, Docker, or similar tools for reproducible ML workflows.
• Ability to move beyond notebooks and contribute to production-ready ML pipelines.
• Strong spoken English - B2+ or higher for explaining model behaviour and technical trade-offs to international teams.

Responsibilities

• Develop and improve ML models for churn prediction, segmentation, forecasting, recommendations, and anomaly detection.
• Work with structured behavioural, transactional, and product usage data.
• Create reproducible training pipelines, feature engineering workflows, and model evaluation reports.
• Collaborate with data engineers on reliable datasets, feature quality, and pipeline monitoring.
• Deploy models as batch jobs or API-based services depending on product requirements.
• Track model performance, drift, quality metrics, and business impact after release.
• Communicate model assumptions, limitations, and results to product and business stakeholders.

Skills

Python

Pandas

NumPy

scikit-learn

PyTorch

SQL

MLflow

Airflow

Docker

Production pipelines

English

Tools

Docker

Cloud platforms

Job description

About the Project

We are looking for a Machine Learning Engineer to work on a demand forecasting and personalisation platform for a subscription-based digital product. The team builds models for churn prediction, user segmentation, recommendation logic, demand trends, and operational forecasting.

The technical environment includes Python, PyTorch or scikit-learn depending on the use case, Pandas, SQL, Airflow, MLflow, Docker, and cloud-based model deployment. The role requires regular communication with international data, product, and engineering teams, so strong spoken English is essential.

What You Will Do

• Develop and improve ML models for churn prediction, segmentation, forecasting, recommendations, and anomaly detection.
• Work with structured behavioural, transactional, and product usage data.
• Create reproducible training pipelines, feature engineering workflows, and model evaluation reports.
• Collaborate with data engineers on reliable datasets, feature quality, and pipeline monitoring.
• Deploy models as batch jobs or API-based services depending on product requirements.
• Track model performance, drift, quality metrics, and business impact after release.
• Communicate model assumptions, limitations, and results to product and business stakeholders.

What We Are Looking For

• 3+ years of commercial experience in machine learning or applied data science.
• Strong Python skills and practical experience with Pandas, NumPy, scikit-learn, and/or PyTorch.
• Good understanding of supervised learning, model validation, metrics, feature engineering, and data leakage risks.
• Experience with SQL and working with production datasets.
• Experience with MLflow, Airflow, Docker, or similar tools for reproducible ML workflows.
• Ability to move beyond notebooks and contribute to production-ready ML pipelines.
• Strong spoken English - B2+ or higher for explaining model behaviour and technical trade-offs to international teams.

Nice to Have

• Experience with time-series forecasting, ranking, recommendation systems, or uplift modelling.
• Experience with cloud platforms such as AWS, GCP, or Azure.
• Experience with feature stores, model monitoring, or A/B testing frameworks.
• Background in subscription products, e-commerce, fintech, adtech, or marketplace analytics.