Senior Applied AI Engineer

About the Company

Responsibilities

• ● Apply machine learning and optimization algorithms to improve the usability and efficiency of the current AutoML and several other user-facing products that will benefit from better classification, regression, forecasting, and recommendations, either classical or based on deep learning.
• ● Build features and run end-to-end systems in a small team of experienced engineers and data scientists.
• ● Shape the direction of our applied ML investment by engaging with engineering and product teams across the company.
• ● Drive the development and deployment of state-of-the-art ML/AI models and systems that directly impact the capabilities and performance of Databricks' products, infrastructure, and services.
• ● Architect and implement robust, scalable ML infrastructure, including model training and serving components to support seamless integration of AI/ML models into production environments.
• ● Work on novel modeling techniques in the field of ML for forecasting.
• ● Contribute to the broader AI community by presenting at conferences and actively participating in open source projects, enhancing Databricks' reputation as an industry leader.

Requirements

• ● 2-8 years of machine learning engineering experience in high-velocity, high-growth companies.
• ● Strong understanding of both computer systems and statistics.
• ● Experience developing AI/ML systems at scale in production.
• ● Strong track record of ML modeling that goes beyond using standard libraries.
• ● Strong coding and software engineering skills, and familiarity with software engineering principles around testing, code reviews, and deployment.
• ● A large breadth of knowledge or willingness to develop mathematical modelling beyond the ML.
• ● Nice to have: experience deploying, scaling, and monitoring models in production.
• ● Nice to have: understanding of the unique infrastructure challenges posed by training and serving predictions in Tier 0 environments.