Applied Scientist/Research Engineer, LLM Training Data

Propio Language Services; Overland Park (KS); On-site; USD 120,000 - 180,000; Full time

Job summary

Propio Language Services seeks an Applied Scientist/Research Engineer, LLM Training Data to own multilingual data strategy and curation pipelines. You will shape annotation workflows, QA rubrics, and evaluation datasets to improve LLM performance, handling data from acquisition to post-training stages.

The role requires building scalable data pipelines, identifying model gaps, and driving data-driven improvements across multilingual AI systems within a fast-growing organization.

Qualifications

• Master’s degree in CS/ML/DS or equivalent practical experience.
• 4+ years in AI data, ML data operations, NLP data engineering, applied ML, or LLM data workflows.
• Strong Python and SQL skills; experience with dataset curation pipelines.
• Experience with annotation workflows, QA rubrics, evaluation datasets, or human-in-the-loop processes.
• Familiarity with multilingual NLP, speech/translation data, low-resource languages, or agentic AI datasets.
• Experience with AWS data and ML tools (S3, Glue, SageMaker, etc.).
• Excellent communication and cross-functional collaboration abilities.

Responsibilities

• Define the end-to-end data roadmap for multilingual and multimodal AI systems.
• Design and build dataset curation pipelines for training, post-training, and evaluation.
• Create annotation schemas, labeling guidelines, QA rubrics, and reviewer workflows.
• Build evaluation datasets and benchmarks; analyze model failure modes.
• Support post-training data workflows such as SFT, instruction tuning, and RLHF-style data.
• Utilize AWS-based data infrastructure to scale secure, traceable AI data workflows.

Skills

Communication skills

Cross-functional collaboration

Education

Master’s degree in CS/ML/DS

PhD in CS/ML/NLP (preferred)

Tools

S3

Glue

SageMaker

Bedrock

Lambda

Step Functions

EKS/ECS

IAM

KMS

Job description

Applied Scientist/Research Engineer, LLM Training Data

Job Type: Full-time

Own Your Impact. At Propio, we don't believe careers happen to people. We believe people create them. Here, you're trusted to make decisions, challenge assumptions, drive innovation, and shape outcomes. Your success is not limited by hierarchy or tenure. It's fueled by your ambition, your curiosity, and your willingness to own your impact. If you're looking for a role where you can simply maintain the status quo, this probably isn't it, but if you're looking for a place where your ideas matter, your growth is accelerated, and your work creates meaningful impact across the world, we'd love to talk.

Why Propio?

Every day, communication changes lives. A patient receives care they otherwise couldn't access. A family gains critical information. A business connects with a customer. A community becomes more inclusive. These moments happen because barriers are removed. And behind those moments are Propio team members who show up every day to solve problems, innovate, and build the future. This isn't just work. This is world impact.

You'll Be Empowered To

• Take ownership of important initiatives and outcomes.
• Drive meaningful business results.
• Influence decisions and contribute new ideas.
• Partner with talented, high-performing team members.
• Challenge yourself through continuous learning and growth.
• Help shape the future of a rapidly growing organization.

What You'll Own

As an Applied Scientist/Research Engineer, LLM Training Data, you will 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.

• 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.

What Makes Someone Successful Here

• Take ownership instead of waiting for direction.
• Embrace challenges as opportunities to grow.
• Continuously seek better ways of working.
• Turn ideas into action.
• Hold themselves and others accountable to high standards.
• Are driven by making a measurable impact.

Required 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.

What You’ll Gain

Own Your Growth: We invest in people who invest in themselves. You'll have opportunities to learn, develop, and expand your capabilities while building a meaningful career.

Own Your Impact: You'll see the connection between your work and our success. We believe great people deserve the opportunity to make a real difference.

Own Your Innovation: The best ideas can come from anywhere. We encourage curiosity, creativity, and challenging the way things have always been done.

Own Your Success: Whether you're building expertise, pursuing leadership opportunities, or expanding your career path, we'll give you room to grow and the support to get there.

At Propio, your work doesn't just move a company forward. It helps connect people, communities, and opportunities across the world. Ready to Build Something Bigger? and discover what happens when you own your success.

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.