Senior ML Engineer - SupportLogic | Scrabble
Posted on July 24, 2024
Job Description
● Ship - Increase velocity of ML model deployment into production through automation of model
management, deployment, and rollout processes.
● Validate - Increase confidence of model rollouts by enriching and automating model validation prior
to and immediately after deployment.
● Measure - Provide insight into accuracy and relevance of ML model predictions in production by
measuring and monitoring model input and output data distributions, as well as user
engagement/feedback on predictions.
● Automate - Incorporate user feedback/activity into new ML model training by automation of data
collection, model retraining, model measurement, etc., towards a goal of continuous automated
model retraining.
● Build - Provide internal tools or incorporate commercial tools (e.g. Kubeflow, VertexAI, LangChain,
LangSmith, etc) into data scientist workflows for data analysis, feature generation, model
development, etc., to boost ML team productivity.
● Collaborate - Bridge the gap between ML research and production-grade backend code by working
with other engineering teams to integrate new ML models or APIs into production.
About you (don't worry if you don't have this whole list- we expect you to learn with us):
● A self-starter, with the interest and passion to contribute in a fast-paced startup environment.
● Provide technical leadership and help drive the team’s ML direction & vision
● B.S. degree or equivalent in Computer Science, Mathematics, or similar field of study.
● Professional experience as a full-time machine learning engineer.
● 5+ years of experience building ML products
● 3+ years of experience using Large Language Models in production
● Strong proficiency in software development and system design
● Fluent in Python
● Experienced with common Python data science libraries such as PyTorch, HuggingFace,
Pandas, numpy and scikit-learn
● Experienced with the lifecycle of model training, evaluation and deployment
● Experienced with using SQL
Preferences:
● Experienced building APIs in Python, particularly in FastAPI or Flask.
● Experienced with using Pytest, Docker, and sqlalchemy
● Experienced with MLOps platforms such as KubeFlow or MLFlow
management, deployment, and rollout processes.
● Validate - Increase confidence of model rollouts by enriching and automating model validation prior
to and immediately after deployment.
● Measure - Provide insight into accuracy and relevance of ML model predictions in production by
measuring and monitoring model input and output data distributions, as well as user
engagement/feedback on predictions.
● Automate - Incorporate user feedback/activity into new ML model training by automation of data
collection, model retraining, model measurement, etc., towards a goal of continuous automated
model retraining.
● Build - Provide internal tools or incorporate commercial tools (e.g. Kubeflow, VertexAI, LangChain,
LangSmith, etc) into data scientist workflows for data analysis, feature generation, model
development, etc., to boost ML team productivity.
● Collaborate - Bridge the gap between ML research and production-grade backend code by working
with other engineering teams to integrate new ML models or APIs into production.
About you (don't worry if you don't have this whole list- we expect you to learn with us):
● A self-starter, with the interest and passion to contribute in a fast-paced startup environment.
● Provide technical leadership and help drive the team’s ML direction & vision
● B.S. degree or equivalent in Computer Science, Mathematics, or similar field of study.
● Professional experience as a full-time machine learning engineer.
● 5+ years of experience building ML products
● 3+ years of experience using Large Language Models in production
● Strong proficiency in software development and system design
● Fluent in Python
● Experienced with common Python data science libraries such as PyTorch, HuggingFace,
Pandas, numpy and scikit-learn
● Experienced with the lifecycle of model training, evaluation and deployment
● Experienced with using SQL
Preferences:
● Experienced building APIs in Python, particularly in FastAPI or Flask.
● Experienced with using Pytest, Docker, and sqlalchemy
● Experienced with MLOps platforms such as KubeFlow or MLFlow