Applied Scientist

Oracle

On-site

Bengaluru, India

Full-time

Senior

Salary not listedPosted 1mo ago

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Real job — pulled straight from Oracle’s careers page · Verified July 15, 2026 · No reposts.

Oracle is hiring a Applied Scientist — a full-time, based in Bengaluru, India role. Apply directly on Oracle's careers page below.

Applied Scientist 3

Location: BENGALURU, KARNATAKA, India

Design and build data-centric GenAI methods for synthetic data generation, multimodal data curation, data augmentation, filtering, deduplication, and quality assessment.
Develop and evaluate synthetic data pipelines for text, speech, vision, and multimodal GenAI use cases, including controllable generation, provenance tracking, safety checks, and domain adaptation.
Build evaluation frameworks that connect data quality to downstream GenAI model performance, including benchmark design, ablation studies, error analysis, and model-feedback loops.
Research and implement modern generative AI techniques, including LLM/VLM-based data generation, fine-tuning, instruction tuning, preference optimization, and model-based data labeling.
Build scalable data and ML pipelines for acquisition, cleaning, transformation, metadata extraction, embedding generation, labeling, training, and evaluation.
Develop production-quality code for batch and real-time ML workflows, including model inference, feature processing, data validation, monitoring, and operational automation.
Translate research papers and emerging GenAI techniques into practical systems that improve data quality, model quality, and customer-facing AI outcomes.
Partner with modeling, product, infrastructure, and domain teams to define GenAI data requirements, quality bars, evaluation criteria, and delivery plans.

Operate across the full lifecycle: research, prototyping, experimentation, productionization, testing, CI/CD, monitoring, runbooks, and production support.

About Us

Only Oracle brings together the data, infrastructure, applications, and expertise to power everything from industry innovations to life-saving care. And with AI embedded across our products and services, we help customers turn that promise into a better future for all. Discover your potential at a company leading the way in AI and cloud solutions that impact billions of lives.

True innovation starts when everyone is empowered to contribute. That’s why we’re committed to growing a workforce that promotes opportunities for all with competitive benefits that support our people with flexible medical, life insurance, and retirement options. We also encourage employees to give back to their communities through our volunteer programs.

We’re committed to including people with disabilities at all stages of the employment process. If you require accessibility assistance or accommodation for a disability at any point, let us know by emailing accommodation-request_mb@oracle.com or by calling 1-888-404-2494 in the United States.

Oracle is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans’ status, or any other characteristic protected by law. Oracle will consider for employment qualified applicants with arrest and conviction records pursuant to applicable law.

Oracle Cloud Infrastructure (OCI) is building applied AI and GenAI systems that operate at enterprise scale. This role sits in the AI organization and focuses on data-centric GenAI: synthetic data generation, data quality, multimodal data curation, evaluation, and scalable pipelines for training and benchmarking frontier models. We are looking for a Senior Applied Scientist who can turn ambiguous data and GenAI model-quality problems into rigorous experiments, reusable systems, and production-ready workflows. The ideal candidate is strong in applied machine learning and GenAI, but is equally interested in the data layer that determines model performance: what data to generate, what to filter, how to measure quality, how to close the loop from model failures back to data, and how to do this reliably at cloud scale.