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Software Engineer - ML OSS

At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers — and customer obsessed — we leap at every opportunity to solve technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started.

As a Software Engineer on the ML OSS Ecosystem Team, you will play a key role in building and maintaining our open-source MLflow platform to support machine learning workflows and enable users to train, deploy and monitor models at scale. Additionally, you will collaborate with the larger ML community to contribute to open-source projects and advance MLOps capabilities in the industry.

The impact you'll have:

  • Design and implement platform capabilities to support the ML development and productionization lifecycle including training, evaluation, deployment, monitoring, and management of machine learning models in MLflow
  • Design and implement MLflow platform integrations with various frameworks in the ML ecosystem
  • Collaborate with the ML community across the world to advance the state-of-the-art in MLOps.
  • Ensure the latest ML tooling advancements are available to Databricks’ customers, thereby enabling organizations around the world to get more value from their data.
  • Mentor and guide junior engineers on the team by helping with project planning, technical decisions, and code and document review.

What we look for:

  • BS (or higher) in Computer Science, or a related field
  • 2+ years of hands-on experience in building production systems using at least one of the following programming languages: Java, Scala, or Python.
  • Experience building and maintaining software tools and frameworks for machine learning, ideally in an open-source environment.
  • Familiarity with ML and MLOps concepts and technologies, such as model training, deployment, and monitoring.
  • [Preferred] Deep understanding and experience in working with Spark and MLflow and should be able to leverage them to build robust and scalable platforms to develop machine learning models
  • [Preferred] Significant contributions to open-source projects in the machine learning domain, such as SparkML, TensorFlow, PyTorch, MLflow, or other similar projects.

About Databricks

Databricks is the data and AI company. More than 9,000 organizations worldwide — including Comcast, Condé Nast, 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.

Compliance

If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.

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