Senior Applied ML Engineer - Drive Scalable AI Solutions

Databricks; United States; Remote; USD 150,000 - 230,000; Full time

Job summary

Databricks is seeking a Senior Applied ML/AI Engineer to apply machine learning and optimization algorithms to improve AutoML and other user-facing products. You will work on classification, regression, forecasting, and recommendations across both classical and deep learning models.

You will build features, deploy state-of-the-art models, and shape ML investment directions by collaborating with engineering and product teams, aiming to make Databricks’ products faster and more cost-efficient.

Qualifications

• 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 beyond standard libraries.
• Strong coding and software engineering skills, testing, reviews and deployment.
• Broad mathematical modelling knowledge beyond ML.
• Nice to have: deployment, scaling, monitoring models in production; Tier 0 infra requirements.

Responsibilities

• Build features and run end-to-end systems in a small team of engineers and data scientists.
• Shape the direction of applied ML investments by engaging with engineering and product teams.
• Drive development and deployment of state-of-the-art ML/AI models and systems across Databricks products and infrastructure.
• Architect robust, scalable ML infrastructure including training and serving components for production environments.
• Work on novel ML modeling techniques for forecasting.
• Contribute to the AI community through conferences and open source involvement.

Skills

ML engineering

Systems & statistics

Production ML systems

Coding & testing

Mathematical modelling

Deployment & monitoring

Job description

Databricks is seeking a Senior Applied ML/AI Engineer to apply machine learning and optimization algorithms to improve AutoML and other user-facing products. You will work on classification, regression, forecasting, and recommendations across both classical and deep learning models.

You will build features, deploy state-of-the-art models, and shape ML investment directions by collaborating with engineering and product teams, aiming to make Databricks’ products faster and more cost-efficient.