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GenAI Senior Machine Learning Engineer, Platform - Databricks

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Job Title
GenAI Senior Machine Learning Engineer, Platform
Job Location
San Francisco, California
Job Description

P-984

Founded in late 2020 by a small group of machine learning engineers and researchers, Mosaic AI enables companies to securely fine-tune, train and deploy custom AI models on their own data, for maximum security and control. Compatible with all major cloud providers, the Mosaic AI platform provides maximum flexibility for AI development. Introduced in 2023, Mosaic AI’s pretrained transformer models have established a new standard for open source, commercially usable LLMs and have been downloaded over 3 million times. Mosaic AI is committed to the belief that a company’s AI models are just as valuable as any other core IP, and that high-quality AI models should be available to all.

Now part of Databricks since July 2023, we are passionate about enabling our customers 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 platform so our customers can use deep data insights to improve their business. We leap at every opportunity to solve technical challenges, striving to empower our customers with the best data and AI capabilities.

Summary: 

Mosaic AI is hiring experienced machine learning platform engineers to build out our generative AI platform for the ML model development lifecycle including pre-training of large language models (LLMs), fine-tuning, evaluation, and serving. You will thrive in this role if you have a strong sense of end-to-end ownership and enjoy translating user requirements into product interfaces and building the backend distributed systems to power those interfaces. In this role, you will have the opportunity to contribute to all areas of our stack such as our CLI and SDK interfaces, backend APIs, distributed orchestration systems built on Kubernetes, and core platform infrastructure to support our product.

You will:

  • Play a key role in the end-to-end design and implementation of our product which is a platform for powering use cases across training and serving of generative AI models
  • Work closely with both ML researchers in the company and customers to identify key areas of development for our generative AI platform
  • Have strong end-to-end product ownership, translating product requirements into user interfaces and backend distributed system design as well as own the implementation of these designs
  • Design and build the core platform infrastructure that supports our customer-facing product features
  • Ensure the reliability, security, and scalability of the backend distributed systems that power all aspects of our product

We look for:

  • 5+ years of full time industry experience
  • Strong software engineering skills
  • Experience building large-scale distributed systems
  • Experience building ML platform systems for applications in the ML model development lifecycle such as model training, data preparation, model evaluation, and model serving
  • Direct experience developing ML models is a plus but not required
  • Strong sense of end-to-end product ownership as well as intuition for both robust system design and product usability
  • Effective communication skills and the ability to articulate complex technical ideas to cross-disciplinary internal and external stakeholders.

We value candidates who are curious about all parts of the company's success and are willing to learn new technologies along the way.

Pay Range Transparency

Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents base salary range for non-commissionable roles or on-target earnings for commissionable roles.  Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks utilizes the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here.

 

Local Pay Range
$166,000$225,000 USD

About Databricks

Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on TwitterLinkedIn 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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Databricks Headquarters Location

San Francisco, CA

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Databricks Company Size

Between 4,000 - 20,000 employees

Databricks Founded Year

2013

Databricks Total Amount Raised

$4,181,559,040

Databricks Funding Rounds

View funding details
  • Series I

    $684,559,082 USD

  • Series H

    $1,600,000,000 USD

  • Series G

    $1,000,000,000 USD

  • Series F

    $400,000,000 USD

  • Series E

    $250,000,000 USD

  • Series D

    $140,000,000 USD

  • Series C

    $60,000,000 USD

  • Series B

    $33,000,000 USD

  • Series A

    $14,000,000 USD