Job Location: Remote
While candidates in the listed location(s) are encouraged for this role, candidates located anywhere in the Continental United States will be considered.
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
- [Preferred] Experience working with Apache Spark to process large-scale distributed datasets
Benefits
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Comprehensive health coverage including medical, dental, and vision
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401(k) Plan
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Equity awards
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Flexible time off
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Paid parental leave
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Family Planning
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Gym reimbursement
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Annual personal development fund
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Employee Assistance Program (EAP)
About Databricks
Databricks is the lakehouse company. More than 7,000 organizations worldwide — including Comcast, Condé Nast, H&M and over 50% of the Fortune 500 — rely on the Databricks Lakehouse Platform to unify their data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe. Founded by the original creators of Apache Spark™, Delta Lake and MLflow, Databricks is on a mission to help data teams solve the world’s toughest problems. To learn more, follow Databricks on Twitter, LinkedIn and Facebook.
Our Commitment to Diversity and Inclusion
At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.
About Databricks
Databricks is the lakehouse company. More than 7,000 organizations worldwide — including Comcast, Condé Nast, H&M and over 50% of the Fortune 500 — rely on the Databricks Lakehouse Platform to unify their data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe. Founded by the original creators of Apache Spark™, Delta Lake and MLflow, Databricks is on a mission to help data teams solve the world’s toughest problems.
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