Machine Learning Engineer

Summary

Join one of our client's teams as a skilled Machine Learning Engineer! This remote position, open to professionals in Argentina or Uruguay, requires alignment with EST time. You will collaborate with data scientists, engineers, and platform engineers to develop and deploy machine learning models and pipelines for various classification projects. The role involves building and maintaining MLOps infrastructure, optimizing models, integrating them into production systems, and staying current with advancements in the field. You'll also contribute to improving data management and code quality. This is a great opportunity to work with autonomy and efficiency in a supportive and challenging environment.

Requirements

• 2+ years of experience in machine learning engineering or a related role
• Strong proficiency in Python programming
• Experience with machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn
• Familiarity with cloud platforms like AWS, including services like SageMaker, S3, and Secrets Manager
• Experience with data processing, cleaning, and feature engineering for structured and unstructured data
• Knowledge of software development best practices, including version control (Git), testing, and documentation
• Excellent problem-solving and debugging skills
• Strong communication and collaboration abilities
• Ability to work independently and take ownership of projects

Responsibilities

• Develop efficient, clean, and maintainable Python code for machine learning pipelines, leveraging our in-house libraries and tools
• Collaborate with the team on code reviews to ensure high code quality and adhere to best practices established in our shared codebase
• Contribute to building and maintaining our MLOps infrastructure from the ground up, with a focus on extensibility and reproducibility
• Take ownership of projects by gathering requirements, creating technical design documentation, breaking down tasks, estimating efforts, and executing with key performance indicators (KPIs) in mind
• Optimize machine learning models for performance and scalability
• Integrate machine learning models into production systems using frameworks like SageMaker
• Stay up-to-date with the latest advancements in machine learning and MLOps
• Assist in improving our data management, model tracking, and experimentation solutions
• Contribute to enhancing our code quality, repository structure, and model versioning
• Help identify and implement the best practices for ML services deployment and monitoring
• Collaborate on establishing CI/CD pipelines and promoting deployments across environments
• Address technical debt items and refactor code as needed

Preferred Qualifications

• Experience with Infrastructure as Code (IaC) tools, preferably Pulumi or Terraform
• Experience with classification models and libraries such as XGBoost, SentenceTransformers, or LLMs
• Knowledge of data versioning, experiment tracking, and model registry concepts
• Familiarity with data pipeline and ETL tools like Dagster, Snowflake, and DBT
• Experience with monitoring logs, metrics, and performance testing for batch inference workloads
• Contributions to open-source machine learning projects
• Experience with deploying and monitoring machine learning models in production

Benefits

• Remote work, flexible hours
• Opportunities, learning, growth, expansion and challenging projects
• Working with autonomy and efficiency