The company is looking to improve user engagement and satisfaction through the development of sophisticated machine learning models and infrastructure.
Requirements
- Strong programming skills in Python and SQL
- Experience with Docker, Kubernetes, Terraform, and scalable deployment tools
- Hands-on experience building CI/CD pipelines for ML systems
- Proficiency in orchestration tools like Airflow, Kubeflow, or Dagster
- Experience working on or contributing to dbt projects
- Comfort working in cloud environments like AWS, GCP, or Azure
- Familiarity with ML frameworks such as PyTorch, TensorFlow, Keras, or similar
Responsibilities
- Build and optimize end-to-end machine learning pipelines from data ingestion to deployment
- Work closely with Product, Marketing, and Operations teams to align ML solutions with business goals
- Improve our ML platform and deploy infrastructure using MLOps best practices
- Evaluate and integrate new tools, models, and frameworks to enhance scalability and performance
- Clearly communicate technical concepts to both technical and non-technical stakeholders
- Document your systems and workflows using Git, Confluence, and related tools
- Scale our ML platform to support future efforts
Other
- 3+ years of professional experience as a Machine Learning Engineer or in a similar role
- A background in Computer Science, Data Science, Engineering, or a related technical field
- Passionate about putting machine learning into production and making personalization work at scale
