Machine Learning Engineer

Job Description

Clara is seeking a Machine Learning Engineer to join its Data Team and play a key role in the ML data architectural design. The company is the leading B2B spend management platform in Latin America, serving over 20,000 companies. The ideal candidate will collaborate with data engineers and data scientists to create, deploy, and maintain complex ML systems and services.

This role involves developing data pipelines for ML, designing, implementing, and deploying ML models and infrastructure, improving existing processes, and creating data models. The engineer will also be responsible for the maintenance and operation of these models and data pipelines, and will collaborate with other teams to provide high-quality ML models.

Clara offers a competitive salary, stock options, flexible hours, a multicultural team environment, an annual learning budget, and a high-ownership culture.

Responsibilities:

• Build, integrate, and maintain ML feature data pipelines.
• Deploy, monitor, and operate ML model lifecycle (ML Ops).
• Collaborate with data scientists in the implementation of ML models.
• Ensure and monitor data quality, and resolve any issues detected.
• Consume data from Data Vaults and Star Schema lakehouse.
• Collaborate with teams remotely.
• Stay up to date with the latest technologies and look for ways to implement them.
• Document your work for future reference.
• Code review and peer programming activities.
• Enforce team best practices and DevOps as a culture within the team.
• Enforce Data Security and Data Privacy best practices.

Requirements:

• 4 years of experience in Model deployment and infrastructure.
• Proficiency in Python and SQL.
• ML Algorithms experience, both theoretical and practical.
• Experience with CI/CD tools (GitHub Actions, GitLab CI, Jenkins, etc.).
• Experience using Github flow.
• Docker container experience.
• Experience implementing the feature store paradigm.
• Experience deploying streaming ML models with common ML frameworks (e.g. xgboost, scikit-learn, catboost).
• High-level understanding of Distributed Systems, and Spark Architecture.
• Experience integrating data from Databases, APIs, and Event Streams (kinesis, kafka).
• Experience developing complex data pipelines (ETL/ELT) with orchestration tools (e.g. Apache Airflow, AWS Glue Workflow, AWS Step Functions, etc.)
• Experience with AWS services: Redshift, Glue, Sagemaker, Lambda, Athena, S3, Kinesis. Or other cloud equivalent services.
• Experience with big data technologies and frameworks (e.g. (py)Spark, Scala, Hive, Kafka, etc.)
• Good English Level.

What Clara Offers:

• Competitive salary + Stock Options (ESOP) from Day 1
• Flexible hours
• Multicultural team with daily exposure to Portuguese, Spanish, and English
• Annual learning budget + internal development paths
• High-ownership culture: we move fast, learn fast, and raise the bar — together