Machine Learning Engineer

Key Skills

C Programming, C++, Cloud computing, Data science techniques, Java Programming, Machine learning techniques, MATLAB Programming, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

This role can can be remote.

The Machine Learning (ML) Practice team is a highly specialized customer-facing machine learning team at Databricks. We deliver professional services engagements to help our customers build, scale, and optimize ML pipelines, as well as put those pipelines into production. We also teach courses on production ML, distributed ML, and deep learning (DL). We work cross-functionally to shape long-term strategic priorities and initiatives alongside engineering, product, and developer relations, as well as support internal subject matter expert (SME) teams. We view our team as an ensemble - we look for individuals with strong, unique specializations to improve the overall strength of the team. This team is the right fit for you if you love working with customers, teammates, and fueling your curiosity for machine learning.

The impact you will have:

• Build, scale, and optimize customer data science workloads and apply best in class MLOps to productionize these workloads across a variety of domains
• Deliver virtual trainings on distributed ML/DL to customers
• Present at conferences such as Data+AI Summit
• Provide technical mentorship to the larger ML SME community in Databricks
• Collaborate cross-functionally with the product and engineering teams to define priorities and influence product roadmap 

What we look for:

• 4+ years of hands-on industry data science experience, leveraging typical machine learning and data science tools including pandas, scikit-learn, and TensorFlow/PyTorch
• Experience building production-grade machine learning pipelines on AWS, Azure, or GCP
• Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience
• Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike
• Passion for collaboration, life-long learning, and driving business value through ML
• Ability to travel to customers up to 30% of the time
• [Preferred] Experience working with Apache Spark to process large-scale distributed datasets

Benefits

• Comprehensive health coverage including medical, dental, and vision
• 401(k) Plan
• Equity awards
• Flexible time off
• Paid parental leave
• Family Planning
• Gym reimbursement
• Annual personal development fund
• Work headphones reimbursement
• Employee Assistance Program (EAP)
• Business travel accident insurance
• Mental wellness resources