Machine Learning Engineer

I’m helping VARTEQ Inc. find a top candidate to join their team full-time for the role of Machine Learning Engineer.

You'll engineer scalable recommender systems, driving client success across global enterprise commerce.

Compensation:

Hidden

Location:

Remote: Argentina

Mission of VARTEQ Inc.:

"To deliver innovative, high‑quality software and technology solutions that help businesses solve complex challenges and achieve digital transformation with efficiency and creativity."

What makes you a strong candidate:

• You have +5 years experience in Machine learning.
• You are proficient in scikit-learn, XGBoost, TensorFlow, Recommender systems, Python.
• English - Conversational

Responsibilities and more:

We are a technology consultancy working with enterprise B2B clients across the US and Europe, including manufacturing, distribution, and high-tech industries. Our teams build and integrate complex digital commerce solutions on platforms like SAP, Salesforce, and Shopify. The project is long-term and actively growing.

Your Responsibilities:

- Designing, building, and optimizing machine learning models for production use, with a focus on recommender systems.

- Develop and maintain scalable ML pipelines, including data processing, training, evaluation, and deployment.

- Work with large datasets to extract insights and improve model performance.

- Collaborate with cross-functional teams to integrate ML solutions into production systems.

- Continuously improve model performance through experimentation, tuning, and monitoring.

- Ensure reliability and scalability of ML systems in cloud environments.

Qualifications:

- 5+ years of hands-on experience in machine learning engineering.

- Strong proficiency in Python and core ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn, XGBoost, etc.).

- Solid experience with deep learning, including model architecture, training, and optimization.

- Proven experience designing and deploying recommender systems.

- Hands-on experience with AWS SageMaker and the broader AWS ML ecosystem.

- Practical experience building and maintaining data pipelines and ML workflows.

- Experience working with production ML systems and MLOps practices.

What We Offer:

- Fully remote work.

- International team with clear processes.

- Paid vacation, holidays, and sick leave.