Senior GenAI Research Scientist - AI Efficiency & Optimization

Job Description At Databricks, we are obsessed with enabling data teams to solve the world’s toughest problems, from security threat detection to cancer drug development. We do this by building and running the world’s best data and AI platform so our customers can focus on the high‑value challenges that are central to their own missions. The Databricks AI Research organization enables companies to develop AI models and agents using their own data, with technologies ranging from post‑training open source LLMs to developing advanced multi‑agent architectures. Databricks AI does so by producing novel science and putting it into production. As a Sr. Research Scientist on the Scaling team, you will be responsible for keeping up with the latest developments in deep learning and advancing the scientific frontier by creating new techniques that go beyond the state of the art. You will work together on a collaborative team of researchers and engineers with diverse backgrounds and technical training. And most importantly, you will love our customers: our goal is to make our customers successful in applying state‑of‑the‑art LLMs and AI systems, and we encode our scientific expertise into our products to make that possible. The Impact you will have As a Sr. Research Scientist on the AI Research Team at Databricks, you will: Define and lead independent research agendas on foundation model efficiency in model training and reinforcement learning, conducting experiments to empirically validate hypotheses and benchmark against state‑of‑the‑art approaches Drive algorithmic innovations for large‑scale neural network training or inference (e.g., novel optimizers, low‑precision techniques, model adaptation methods) Optimize ML systems for distributed training, memory efficiency, and compute efficiency through hands‑on implementation. What We Look for MS/PhD in Computer Science or related field with strong foundations in machine learning and systems Proven ability to write high‑quality, efficient code in Python and PyTorch for research implementation and experimentation Strong preference for candidates with first‑author publications at top ML/systems conferences (ICLR, ICML, NeurIPS, MLSys) focused on optimization or efficiency. Pay Range Transparency Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected 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 anticipates utilizing 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 – $230,000 USD Benefits At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here. 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. #J-18808-Ljbffr Menlo Ventures