Machine Learning Engineer - Remote

Overview

Kiddom is a groundbreaking educational platform that promotes student equity and growth by uniting high-quality instructional materials with dynamic digital learning. Through unparalleled curriculum management functionality, Kiddom empowers schools and districts to take ownership of their curriculum – resulting in learning experiences tailored to meet the unique needs and goals of local communities. Kiddom’s high-quality curriculum is layered with robust teacher and leader data insights to drive the continuous improvement of instructional decisions, school/district programming, and professional learning.

In Short

• Design, build, and maintain scalable data pipelines to transform raw data into analytics-ready datasets.
• Ensure optimal performance, reliability, and efficiency of the data pipelines.
• Integrate machine learning models into data pipelines to enhance analytics capabilities.
• Collaborate with data scientists to deploy and monitor ML models in production.
• Ensure the scalability and reliability of ML workflows and infrastructure.
• Develop and optimize ML models for predictive analytics and data-driven decision-making.
• Monitor the data infrastructure for performance bottlenecks and implement optimizations as necessary.
• Collaborate with other engineering teams to ensure seamless data integration with high availability.

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• 3+ years of experience as a data engineer, and 8+ years of software engineering experience (including data engineering).
• Expertise in using Amazon SageMaker for building, training, and deploying machine learning models.
• Knowledge of AWS Lambda for serverless execution of code, especially for model inference and lightweight processing tasks.
• Familiarity with AWS Glue or similar ETL tools (Extract, Transform, Load).
• Familiarity with Snowflake, RDS, Cassandra database services for structured data storage and querying.
• Proficiency in using Amazon S3 for data storage and retrieval, especially for large datasets used in machine learning.
• Knowledge of AWS EC2 for scalable computing resources and ECS for containerized application deployment.
• Understanding of AWS Identity and Access Management (IAM) for managing permissions and security.
• Familiarity with Amazon Kinesis for real-time data streaming and processing.
• Skills in preprocessing and transforming raw data into a format suitable for machine learning using DBT.
• Experience with CI/CD tools and practices for automating the deployment and monitoring of machine learning models.
• Knowledge of AWS CloudWatch and AWS CloudTrail for monitoring model performance and logging events.
• Proficiency in using AWS CloudFormation or Terraform to manage and provision AWS resources programmatically.
• Strong programming skills in Python.
• Proficiency in SQL for querying databases and manipulating structured data.
• Understanding of security best practices in AWS, including data encryption and network security.
• Knowledge of AWS cost management and optimization strategies to ensure efficient use of resources.
• Experience in developing and deploying APIs for model inference and interaction with other systems using AWS API Gateway and AWS Lambda.

Benefits

• Competitive salary
• Meaningful equity
• Health benefits: medical (various PPO/HMO/HSA plans), dental, vision, disability and life insurance
• 10 paid sick days per year
• Unlimited vacation time policy (subject to internal approval). Average use 4 weeks off per year.
• Paid family leave for eligible employees