Applied Scientist 3

Job Description

• 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.
  
  
  

Responsibilities

• 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.
• Experience building scalable data or ML pipelines using distributed compute, cloud storage, batch processing, or workflow orchestration.
  
  
  

Qualifications

Career Level - IC3

About Us

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