
Senior Software Engineer, Localization Engineering
Role summary
Autodesk is seeking a Senior Software Engineer specializing in Localization Engineering to design and scale advanced AI-driven localization systems. This role requires expertise in NMT, LLMs, QE, and APE, combined with strong backend engineering and localization workflow knowledge. You will architect next-generation localization pipelines, build scalable backend services and APIs, and ensure quality and automation across enterprise-scale products. Responsibilities include fine-tuning transformer models, optimizing cloud deployments on AWS, and developing automation frameworks for content extraction, translation, and QA. The role involves serving as a technical authority for localization standards and AI translation quality, partnering with program management, and providing guidance to internal teams and suppliers.
Job Requisition ID #
26WD95474
Position Overview
Autodesk is 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 Autodesk’s AI-powered translation ecosystem.
This role is based in Vancouver, British Columbia, Canada.
Responsibilities
- 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 Autodesk translation memory and glossary data to capture domain-specific voice and terminology.
- 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).
- 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.
- 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
- Bachelor’s degree in Computer Science, Machine Learning, Computational Linguistics, or related field (or equivalent experience)
- 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
**Learn More**
About Autodesk
Welcome to Autodesk! Amazing things are created every day with our software – from the greenest buildings and cleanest cars to the smartest factories and biggest hit movies. We help innovators turn their ideas into reality, transforming not only how things are made, but what can be made.
We take great pride in our culture here at Autodesk – it’s at the core of everything we do. Our culture guides the way we work and treat each other, informs how we connect with customers and partners, and defines how we show up in the world.
When you’re an Autodesker, you can do meaningful work that helps build a better world designed and made for all. Ready to shape the world and your future? Join us!
Salary transparency
Salary is one part of Autodesk’s competitive compensation package. For Canada-BC based roles, we expect a starting base salary between $107,000 and $157,300. Offers are based on the candidate’s experience and geographic location, and may exceed this range. In addition to base salaries, our compensation package may include annual cash bonuses, commissions for sales roles, stock grants, and a comprehensive benefits package.
Diversity & Belonging
We take pride in cultivating a culture of belonging where everyone can thrive. Learn more here: https://www.autodesk.com/company/diversity-and-belonging
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Sample Autodesk interview questions
- 1
Design a distributed rate limiter.
system designmedium - 2
Outline the components of a distributed A/B testing system that ensures statistical validity and prevents experiment collision.
system designmedium - 3
Reverse Nodes in k-Group Reverse nodes in k-group in a linked list. Input: head = [1,2,3,4,5], k = 3 Output: [3,2,1,4,5] Explanation: The first 3 elements are reversed, while the remaining 2 are left untouched since they don't form a complete group.
codingmedium - 4
Split an array into consecutive subsequences. Input: nums = [1,2,3,4,4,5] Output: FALSE Explanation: The numbers can form [1,2,3,4], but the remaining leftover group [4,5] is too short to form a valid sequence of length 3.
codingmedium - 5
Count the number of anagrammatic substrings from one string present in another. Input: s = "abab", p = "ab" Output: [0, 1, 2] Explanation: The substrings "ab", "ba", and "ab" starting at indices 0, 1, and 2 respectively are all anagrams of the string "ab".
codingmedium
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