Principal Research Scientist – Scaling

Remote, USA • Full-time • Posted 2026-05-31

Lead and grow a multidisciplinary research team focused on LLM scaling, efficiency, and systems performance.
Define and execute the scaling research roadmap in alignment with Databricks’ strategic objectives.
Drive algorithmic innovations for large-scale training and inference, including optimizers, low-precision techniques, and model adaptation methods.
Oversee the design and execution of large-scale experiments and benchmark results against state-of-the-art methods.
Optimize distributed training, parallelism, memory management, and hardware utilization in collaboration with systems and infrastructure teams.
Translate research breakthroughs into customer-facing capabilities in the Databricks AI platform.
Establish metrics, evaluation protocols, and best practices for scaling-focused research and drive adoption across the organization.
Champion responsible deployment by ensuring model behavior, reliability, and safety remain first-class considerations.
Work hands-on with the team to develop high-quality Python and PyTorch code for research, prototyping, and production integration.
Mentor and develop research scientists and engineers through technical guidance and career support.

Proven ability to lead a research team developing novel techniques for foundation model efficiency or related topics.
Strong track record of industry impact.
Deep expertise in at least one of: generative AI, LLMs, distributed ML systems, model optimization, or responsible AI.
Strong emphasis on scaling and efficiency for large-scale neural networks.
Strong programming skills and demonstrated ability to write high-quality, efficient code in Python and PyTorch.
Demonstrated ability to translate research innovation into scalable product capabilities with product and engineering teams.
Excellent communication, leadership, and stakeholder management skills.
Experience influencing cross-functional roadmaps and aligning research with business impact.
Prior work at the intersection of systems and ML, such as distributed training frameworks, compiler and kernel optimization, or memory-/compute-efficient model design (preferred).
Strong industry and academic network in large-scale ML, with ongoing collaborations or conference service such as PC or area chair roles (preferred).
First-author publications at top ML/systems conferences such as ICLR, ICML, NeurIPS, or MLSys, or influential open-source contributions / widely used deployed systems, especially in optimization or efficiency (preferred).

Competitive base salary range of $280,000 to $350,000 USD.
Eligibility for an annual performance bonus.
Eligibility for equity as part of the total compensation package.
Comprehensive benefits and perks offered regionally.
Compensation may be adjusted based on skills, experience, certifications, training, and work location.

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