This is a remote position.
What You'll Do
Develop, deploy, and maintain production machine learning models.
Perform exploratory data analysis and feature engineering to extract actionable insights.
Build and improve data pipelines that support ML workflows and automation.
Collaborate with engineering and business stakeholders to deliver scalable ML solutions.
Contribute to AI/LLM-based capabilities where applicable to enhance product offerings.
Monitor and optimize model performance in production environments (not only notebook-based development).
Document all procedures, configurations, and changes in a clear, auditable manner.
What You'll Need
2+ years of experience building and maintaining production machine learning models.
Strong Python programming skills.
Experience with AWS SageMaker or another enterprise ML platform (e.g., Vertex AI or Azure ML) supporting production ML pipelines.
Experience deploying and monitoring ML models in production (not only notebook-based development).
Advanced SQL.
Git/version control.
Ability to work independently in production environments.
Experience with MLOps tools (MLflow, Airflow, dbt, or similar).
Snowflake.
Marketing, growth, experimentation, or causal inference experience.
Experience with LLMs or AI agents.
Agile development practices.
Experience building scalable ML solutions in enterprise environments.
Familiarity with cloud-based ML platforms and production deployment best practices.
Strong communication skills and ability to work with cross-functional teams.
Familiarity with US time zones (PST to EST) and remote collaboration workflows.
Be part of a remote-first organization where flexibility is embraced.
Work and learn alongside talented engineers and technology leaders.
Explore opportunities to learn and grow through technical and non-technical training programs.
Gain global exposure by working on products with international teams and clients.
Attend virtual and in-person international technology conferences to expand your knowledge and network.
