Hiring for a 12 Months Hybrid role in Vancouver, BC (Work in PST hours).
We are seeking a Senior Software Engineer, Localization Engineering to architect and scale the next generation of intelligent localization systems. This role combines deep expertise in AI-driven translation technologies with strong backend engineering and localization workflow knowledge.
You will lead the design of advanced localization pipelines that integrate Neural Machine Translation (NMT), Large Language Models (LLMs), Quality Estimation (QE), and Automated Post-Editing (APE) into production-ready systems. At the same time, you will serve as the technical authority for localization implementation across assigned products or content areas, ensuring quality, scalability, and automation at enterprise scale.
Working closely with Localization Program Management, product engineering teams, and external suppliers, you will build systems that enable efficient content extraction, translation, validation, and release - while advancing our AI-powered translation ecosystem.
Top 3 Must haves:
- Strong proficiency in Python and experience with transformer-based models
- Experience training, fine-tuning, and deploying models
- Strong understanding of end-to-end localization workflows
Key Responsibilities:
AI-Powered Localization Architecture
- Architect next-generation localization workflows combining NMT and LLM-based refinement using frameworks such as LangChain.
- Design and implement reference-free Quality Estimation (QE) systems using COMET, BLEURT, or custom LLM evaluators to predict translation quality without reference data.
- Develop and fine-tune Automated Post-Editing (APE) solutions to improve translation fluency, terminology accuracy, and stylistic consistency.
- Fine-tune transformer-based models (Hugging Face) using our translation memory and glossary data to capture domain-specific voice and terminology.
Engineering & System Integration
- Build scalable backend services, APIs, and microservices to support automated localization workflows.
- Design integrations between product systems, TMS platforms, content repositories, and CI/CD pipelines.
- Develop automation frameworks for content extraction, file processing, translation handoff, validation, and linguistic QA.
- Optimize model performance, inference latency, and cloud deployment on AWS (e.g., SageMaker, Bedrock).
Data & NLP Engineering
- Build Python pipelines for multilingual data curation, sentence alignment, tokenization, and fuzzy matching.
- Benchmark translation performance using BLEU, chrF++, TER, COMET, and other NLP metrics.
- Improve workflow efficiency and scalability through intelligent automation and system enhancements.
Technical Leadership
- Serve as the technical authority for localization engineering standards and AI translation quality.
- Partner with Localization Program Leads to align engineering execution with localization strategy.
- Provide guidance to internal teams and suppliers on tooling, automation, and AI-enabled localization practices.
- Contribute to the evolution of shared localization tools, frameworks, and technical standards.
Minimum Qualifications:
- 6+ years of software engineering experience, with significant backend or AI/ML focus
- Strong proficiency in Python and experience with transformer-based models (Hugging Face or similar)
- Experience training, fine-tuning, and deploying models using AWS (e.g., SageMaker, Bedrock)
- Experience building APIs, microservices, and system integrations
- Strong understanding of end-to-end localization workflows (extraction, translation, review, QA, release)
- Experience with translation management systems (e.g., Phrase, Passolo, or similar).
- Experience with databases (SQL / NoSQL) and cloud-native architectures.
Preferred Qualifications:
- Experience implementing reference-free Quality Estimation or Automated Post-Editing systems
- Familiarity with LLM orchestration frameworks (e.g., LangChain)
- Experience integrating localization workflows into CI/CD pipelines
- Knowledge of automated linguistic QA and validation tooling
- Background in NLP, computational linguistics, or multilingual data engineering.
- Experience optimizing model inference and cost-performance in production environments.
