Propio Language Services is a provider of the highest quality interpretation, translation, and localization services. Our people take pride in every resource we offer, and our users always have access to cutting-edge technology, exceptional support, and collaborative user experiences. We are driven by our passion for innovation, growth, and bridging communication gaps in a diverse world. If you’re passionate about delivering technology-driven solutions and building lasting client relationships while contributing to client growth, Propio could be the ideal place for you.
We are building AI-powered systems that enhance multilingual communication, improve interpreter workflows, and support next-generation AI applications across text, speech, and multimodal experiences.
Propio is hiring an AI Data Strategy Engineer / Applied Scientist, LLM 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 model 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 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.
