Mutt Data

Machine Learning Engineer Lead

Remote Machine Learning Engineering role with clear candidate location fit.

PostedJul 3, 2026

Eligible countries1 accepted country

Seniority signalSenior, Lead

Work settingRemote

Accepted candidate locations

Argentina

Role overview

Readable role content extracted into sections for faster review.

Leveraging our expertise, we build modern Machine Learning systems for demand planning and budget forecasting.
Developing scalable data infrastructures, we enhance high-level decision-making, tailored to each client.
Offering comprehensive Data Engineering and custom AI solutions, we optimize cloud-based systems.
Using Generative AI, we help e-commerce platforms and retailers create higher-quality ads, faster.
Building deep learning models, we enhance visual recognition and automation for various industries, improving product categorization, quality control, and information retrieval.
Developing recommendation models, we personalize user experiences in e-commerce, streaming, and digital platforms, driving engagement and conversions.

Lead and mentor the team, fostering skill development and best practices.
Guide the creation of SPECs and ensure clear documentation.
Act as the main point of contact for stakeholders, ensuring alignment between technical and business goals.
Develop and optimize data and ML solutions, integrating best practices for scalability and monitoring.
Oversee the full lifecycle of data pipelines and models—from EDA and feature engineering to deployment and continuous improvement.
Build Proof of Concepts (POCs) to validate and refine solutions.
Optimize performance, latency, memory, and throughput across data systems.
Design and maintain feature stores and data pipelines to support workflows.
Research and implement emerging technologies to enhance solutions.
Participate in candidate interviews and evaluations to help Mutt grow.

Experience leading and mentoring teams in Data projects, guiding technical decisions, and fostering team growth.
Proven ability to collaborate with stakeholders, translate business needs into technical solutions, and align priorities across teams.
Experience implementing ML-based systems, including model lifecycle management, monitoring, and MLOps pipeline setup.
Strong proficiency in Python (Pandas, Numpy, Jupyter, Scikit-Learn, XGBoost, Plotly).
Knowledge of SQL.
Experience with cloud platforms (AWS, GCP, Azure).

Experience with ML workflows (Airflow, MLflow, H2OAI, Databricks).
Background in AdTech or Generative AI projects.
Understanding of Deep Learning frameworks (Keras, PyTorch, TensorFlow).
Solid command of English for understanding and communicating technical concepts (Design Documents, etc.).
Basic knowledge of Docker.