Who is Gaggle?  We protect children and save lives. Our technology analyzes unstructured data created by students (email, documents, chats, images, video, etc) for the purpose of identifying signs of child abuse, bullying, suicide, and other serious concerns that affect today's youth. At Gaggle, WE COME AS WE ARE, quirks and all. We believe that everyone has something unique to offer, and we can all learn and grow from each other's experiences.

We are looking for a 100% REMOTE Junior MLOps Engineer.  You will assist our Senior MLOps Engineer and Data Scientists with improving upon and developing new automation and monitoring for our ML models, with an eye toward performance, accuracy, and cost effectiveness.

You will be part of a small team that designs and implements innovative ways to better solve this problem. We have a number of challenges in both the quantity/quality of data we analyze, and the time-frame in which we must make accurate predictions to better allow our 24-hour monitoring staff to make decisions on how best to intervene in serious cases.

If you are driven by a desire to solve interesting problems and do some good in this world at the same time, Gaggle may be the place for you!

NOTE: At this time, we are only able to consider candidates that are citizens of the United States (or have an active Green Card (https://www.uscis.gov/green-card)) and living in one the 50 states (https://en.wikipedia.org/wiki/U.S._state).

JOB DESCRIPTION AND RESPONSIBILITIES

• Develop automation around training, evaluation, and deployment of ML models.
• Collaborate with data scientists to define and manage model productionalization and platform release plans.
• Develop and maintain data warehouses to allow data scientists easy access to query the data they need.
• Deploy REST APIs on top of fitted models using distributed computation to support real-time, client-facing integration.
• Monitor and tune services for performance and cost savings.
• Participate in ML software development and maintenance.
• Discover and implement tools to enable data scientists to do their jobs more efficiently.
• Maintain good documentation on projects.

ESSENTIAL SKILLS REQUIRED

• A curious mind and a desire to learn new things, particularly when it comes to Machine Learning and automation.
• Experience with Cloud providers, preferably AWS.
• Knowledge of Continuous Integration / Continuous Deployment practices.
• Some experience building CI/CD pipelines with modern tooling, such as AWS CodePipeline, Jenkins or Bamboo.
• Be familiar with Python. Most of our code is in Python, with a smattering of Java.
• Experience with shell scripting/command line tools in a Mac and/or Linux environment.
• Be able to summarize, and communicate technical challenges and solutions effectively.
• Have good written and verbal communication skills.
• Ability to work independently and as part of a team.
• A Bachelor's degree in Computer Science or a related field, or 2+ years of full-time experience in MLOps or a related position, such as DevOps Engineer or ML Engineer/Data Scientist.

PREFERRED SKILLS

• Proficiency with Python or a similar language, such as Ruby or NodeJS.
• Some familiarity with AWS services, particularly SageMaker, ECS, Lambda, Code* Suite (CodePipeline, CodeBuild, etc.).
• Familiarity with Docker.
• Familiarity with Git/GItHub.
• Familiarity with the Hugging Face AI community.
• Familiarity with Jupyter Notebooks.
• Familiarity with monitoring tools, such as DataDog or CloudWatch.
• Familiarity with Deep Learning fundamentals and statistics underlying Machine Learning.
• Some experience with creating and serving REST APIs.

BONUS SKILLS

• AWS Certification (any) is a big plus.
• Hands-on experience with AWS SageMaker.
• Experience with experiment tracking tools such as MLFlow, ClearML, or Sagemaker Experiments.
• Experience with distributed computing tools such as AWS EMR, Apache Spark, or AWS Glue.