AI Data Strategy Engineer / Applied Scientist, LLM Data

Key Responsibilities

• Define the end-to-end data roadmap for multilingual and multimodal AI systems, including text, speech, translation, interpretation, low-resource languages, and agentic AI workflows.
• Create dataset curation pipelines for training, post-training, and evaluation, including cleaning, deduplication, filtering, PII redaction, quality scoring, sampling, balancing, and versioning.
• Create annotation schemas, labeling guidelines, QA rubrics, golden datasets, and reviewer workflows for multilingual, speech, translation, and agentic AI data.
• Build evaluation datasets and benchmarks, analyze model failure modes, and translate performance gaps into targeted data improvements.
• Support post-training data workflows such as SFT, instruction tuning, preference data, RLHF/DPO-style data, reward model data, and synthetic data generation.
• Use modern annotation tools and AWS-based data infrastructure to scale secure, traceable, and compliant AI data workflows.

Qualifications

• Bachelor’s degree in Computer Science, Machine Learning, Data Science, Computational Linguistics, Linguistics, Statistics, or a related field, or equivalent practical experience.
• 4+ years of experience in AI data, ML data operations, NLP data engineering, applied ML, speech/translation data, or LLM data workflows.
• Strong hands-on experience with Python, SQL, and dataset curation pipelines.
• Experience with annotation workflows, QA rubrics, evaluation datasets, or human-in-the-loop data processes.
• Familiarity with multilingual NLP, speech data, translation data, low-resource languages, conversational AI, or agentic AI datasets.
• Working knowledge of AWS data and ML tools such as S3, Glue, SageMaker, Bedrock, Lambda, Step Functions, EKS/ECS, IAM, or KMS.

Preferred Qualifications

• Master’s or PhD in Computer Science, Machine Learning, NLP, Computational Linguistics, Data Science, Statistics, or a related field.
• Experience with LLM post-training workflows such as SFT, instruction tuning, preference data, RLHF, DPO, reward modeling, or evaluation data generation.
• Experience with synthetic data generation, active learning, weak supervision, LLM-as-judge workflows, or automated data quality scoring.
• Experience with modern annotation and data platforms such as Labelbox, Scale AI, Prodigy, Argilla, Snorkel, Humanloop, or custom internal tooling.