Applied Scientist/Research Engineer, LLM Training Data

Job Type

Full-time

Description

Propio Language Services is a top-five global language services provider and the fastest-growing company in the industry. Operating at nine-figure scale across healthcare, legal, and other sectors, Propio delivers high-quality, real-time multilingual interpretation, translation, and localization services. Driven by cutting-edge technology and exceptional service, we create seamless experiences that bridge communication gaps across languages, cultures, and communication channels.

Propio is hiring an Applied Scientist/Research Engineer, LLM Training Data to own the data strategy, curation pipelines, annotation workflows, and evaluation datasets that power our multilingual AI systems. This is a hands-on technical role for someone who understands how to manage the full AI data lifecycle, from acquisition, curation, annotation, and quality control to evaluation datasets and post-training data, to directly improve LLM performance. The ideal candidate can build scalable data pipelines, design high-quality annotation and QA processes, identify model failure modes, and close performance gaps through targeted data acquisition, curation, and synthetic data generation.

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.
• 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 vision 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.

Requirements

Qualifications:

• Master’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.

Preferred Qualifications

• 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.

Notice of AI Use in Job Application Review

As part of our commitment in creating a fair, efficient, and consistent hiring process we may use artificial intelligence (AI) to help our recruiting teams organize, summarize, and analyze information provided by candidates, including resumes, application responses, and other materials submitted during the application process.AI may be used to identify patterns, highlight relevant skills, and experience, and assist in comparing a candidate’s qualifications with the requirement of a specific role. These tools are to improve efficiency and consistency while supporting more informed hiring decisions, which will ultimately be made by the hiring team.

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