We are seeking a skilled Machine Learning Engineer to join one of our client's teams. In this role, you will work closely with data scientists, data engineers, and platform engineers to develop and deploy machine learning models and pipelines for various classification projects and more.

Key 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

Required Qualifications

3+ 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

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

About RYZ Labs:

RYZ Labs is a startup studio built in 2021 by two lifelong entrepreneurs. The founders of RYZ have worked at some of the world's largest tech companies and some of the most iconic consumer brands. They have lived and worked in Argentina for many years and have decades of experience in Latam. What brought them together is the passion for the early phases of company creation and the idea of attracting the brightest talents in order to build industry-defining companies in a post-pandemic world.

Our teams are remote and distributed throughout the US and Latam. They use the latest cutting edge technologies in cloud computing to create applications that are scalable and resilient. We aim to provide diverse product solutions for different industries, planning to build a large number of startups in the upcoming years.

At RYZ, you will find yourself working with autonomy and efficiency, owning every step of your development. We provide an environment of opportunities, learning, growth, expansion and challenging projects. You will deepen your experience while sharing and learning from a team of great professionals and specialists.

Our values and what to expect:

Customer First Mentality - every decision we make should be made through the lens of the customer
Bias for Action - urgency is critical, expect that the timeline to get something done is accelerated
Ownership - step up if you see an opportunity to help, even if not your core responsibility
Humility and Respect - be willing to learn, be vulnerable, and treat everyone that interacts with RYZ with respect
Frugality - being frugal and cost conscious helps us do more with less
Deliver Impact - get things done in the most efficient way
Raise our Standards - always be looking to improve our processes, our team, our expectations. Status quo is not good enough and never should be

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Machine Learning Engineer