Note: The job is a remote job and is open to candidates in USA. Propio Language Services is a provider of high-quality interpretation, translation, and localization services. They are seeking an AI Data Strategy Engineer / Applied Scientist to manage the data strategy and workflows that power their multilingual AI systems, focusing on enhancing data quality and model performance.
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
- Design and build 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
Skills
- 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
- Strong communication skills and ability to work with ML engineers, applied scientists, product teams, linguists, data teams, and vendors
- 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
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