Applied Scientist 3
On-siteBengaluru, Karnataka, India
Job Summary
Design and build data-centric GenAI methods for synthetic data generation, multimodal curation, augmentation, filtering, and quality assessment. Develop and evaluate synthetic data pipelines for text, speech, vision, and multimodal use cases, including controllable generation, provenance tracking, safety checks, and domain adaptation. Build evaluation frameworks connecting data quality to downstream model performance through benchmark design, ablation studies, and error analysis. Research and implement modern techniques like LLM/VLM-based generation, fine-tuning, instruction tuning, and preference optimization. Develop scalable ML pipelines for acquisition, cleaning, transformation, and evaluation, while producing production-quality code for batch and real-time workflows. Translate research into practical systems that improve data and model quality. Partner with modeling, product, and infrastructure teams to define requirements and delivery plans across the full lifecycle from prototyping to production support.
Required Qualifications
- 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
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