Applied Scientist 3

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.

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

Ph.D. degree, Master's degree, or equivalent experience in computer science, artificial intelligence, machine learning, operations research, statistics, or a related technical field. 

5+ years with a Master's degree or 3+ years with a Ph.D. applying machine learning to real-world problems. 

Strong Python programming skills and experience building production-quality ML, GenAI, or data systems. 

Hands-on experience with PyTorch and modern deep learning stacks; experience with Hugging Face, LLMs, VLMs, diffusion models, or multimodal models is strongly preferred. 

Experience with data-centric AI or GenAI methods such as synthetic data generation, data quality measurement, dataset curation, weak supervision, model-based labeling, active learning, deduplication, or data augmentation. 

Experience designing experiments and interpreting results through statistical analysis, ablation studies, benchmark evaluation, and error analysis. 

Strong understanding of model training, inference, evaluation, and production monitoring.

Ability to read research papers, identify practical value, and implement useful techniques in real systems.

Strong written and verbal communication skills, including technical proposals, design documents, experiment reports, and stakeholder presentations.