Databricks

3 months ago

Principal Research Scientist – Scaling

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 23 hours agoStatus: Live

Job Summary

Lead a multidisciplinary research team focused on foundational and applied AI problems, with an emphasis on LLM scaling, efficiency, and systems performance. Define scaling roadmaps, drive algorithmic innovations, and translate research into scalable Databricks AI platform capabilities while mentoring researchers and collaborating with product and engineering teams.

Location: San Francisco

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Sr. Director level

Key Responsibilities

•Lead and grow a multidisciplinary research team focused on foundational and applied AI problems, with a focus on LLM scaling, efficiency, and systems performance.
•Define the scaling research roadmap aligned with Databricks’ objectives, prioritizing foundation model efficiency and large-scale training and inference.
•Drive algorithmic innovations for large-scale neural network training and inference, including novel optimizers, low-precision techniques, and model adaptation methods, with rigorous empirical validation.
•Optimize end-to-end ML systems for distributed training and RL, memory efficiency, and compute efficiency through collaboration with core systems and platform teams.
•Partner with product and engineering to translate research breakthroughs into customer-impacting capabilities in the Databricks AI platform.

Pay and Benefits

Salary: USD 280,000 - 350,000 annually

Key Requirements

•Lead a multidisciplinary research team focused on foundational and applied AI problems, with emphasis on LLM scaling, efficiency, and systems performance.
•Define the scaling research roadmap in alignment with strategic objectives, prioritizing foundation model efficiency and large-scale training/inference.
•Drive algorithmic innovations for large-scale neural network training and inference, including novel optimizers, low-precision techniques, and model adaptation methods, with rigorous empirical validation.
•Optimize end-to-end ML systems for distributed training and RL, memory and compute efficiency, collaborating with core systems and platform teams to translate ideas into production-ready infrastructure.
•Mentor and develop talent, providing technical guidance and career development support for research scientists and engineers.

Experience:AIMLDistributed systemsLLMs

Skills:PythonPyTorchDistributed systemsLarge language modelsModel optimizationResearch leadership

Languages:English

Tech Stack:PythonPyTorchLLMsDistributed systemsOptimizer designLow-precision training/inference

Company Brief

Databricks

Provides a unified data analytics platform powered by Apache Spark to simplify building, deploying, and scaling data engineering, data science, and machine learning workloads for enterprises.

Industry: Data Infrastructure

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Scaleup

Valuation: Unicorn (USD 1B+)

Funding: Series E+

Headquarters: San Francisco, United States

Founded: 2013

Website LinkedIn

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Databricks

3 months ago

Principal Research Scientist – Scaling

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 23 hours agoStatus: Live

Job Summary

Location: San Francisco

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Sr. Director level

Key Responsibilities

•Lead and grow a multidisciplinary research team focused on foundational and applied AI problems, with a focus on LLM scaling, efficiency, and systems performance.
•Define the scaling research roadmap aligned with Databricks’ objectives, prioritizing foundation model efficiency and large-scale training and inference.
•Drive algorithmic innovations for large-scale neural network training and inference, including novel optimizers, low-precision techniques, and model adaptation methods, with rigorous empirical validation.
•Optimize end-to-end ML systems for distributed training and RL, memory efficiency, and compute efficiency through collaboration with core systems and platform teams.
•Partner with product and engineering to translate research breakthroughs into customer-impacting capabilities in the Databricks AI platform.

Pay and Benefits

Salary: USD 280,000 - 350,000 annually

Key Requirements

•Lead a multidisciplinary research team focused on foundational and applied AI problems, with emphasis on LLM scaling, efficiency, and systems performance.
•Define the scaling research roadmap in alignment with strategic objectives, prioritizing foundation model efficiency and large-scale training/inference.
•Drive algorithmic innovations for large-scale neural network training and inference, including novel optimizers, low-precision techniques, and model adaptation methods, with rigorous empirical validation.
•Optimize end-to-end ML systems for distributed training and RL, memory and compute efficiency, collaborating with core systems and platform teams to translate ideas into production-ready infrastructure.
•Mentor and develop talent, providing technical guidance and career development support for research scientists and engineers.

Experience:AIMLDistributed systemsLLMs

Skills:PythonPyTorchDistributed systemsLarge language modelsModel optimizationResearch leadership

Languages:English

Tech Stack:PythonPyTorchLLMsDistributed systemsOptimizer designLow-precision training/inference

Company Brief

Databricks

Provides a unified data analytics platform powered by Apache Spark to simplify building, deploying, and scaling data engineering, data science, and machine learning workloads for enterprises.

Industry: Data Infrastructure

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Scaleup

Valuation: Unicorn (USD 1B+)

Funding: Series E+

Headquarters: San Francisco, United States

Founded: 2013

Website LinkedIn