In our fast-growing sector, talent is always needed and people with a variety of educational and professional backgrounds are valued. Across many business verticals, localization professionals can choose among many localization careers. They can occupy diverse roles ranging from CEOs to software developers, translators to sales managers, interpreters to consultants, and many more.
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The role of a tester or quality assurance professional is to ensure that any localized product — be it a website, a game, an app or an online marketing campaign — works as well in the target language as it does in the source language. In the localization industry, a difference is made between a software and a linguistic tester. A software tester is responsible for testing the overall functionality of a product or content, runs software builds, and records bugs in bug tracking databases including problems with internationalization, incorrect currencies, country standards, etc. They often rely on automatic testing software to see how the app performs across multiple platforms and devices. A linguistic or language tester is responsible for the linguistic quality of the product or content and checks work received from language service providers. They often work as revisers and use automatic QA tools to detect errors, flag them, and calculate quality scores, or determine if the work should be re-routed back to the language provider for corrections.
In the global language industry industry, sales and account management positions are uniquely located with language service providers. Sales efforts can be divided into those that develop new business and those that cultivate and nurture existing business. Sales managers, also called hunters, find prospects, create new relationships, and bring in fresh accounts to the company. Account managers grow existing business. Since client relationships are already established, the main objective of an account manager is to keep the accounts active and bring in new projects and revenue opportunities.
Localization program managers are responsible for overseeing and managing all aspects of internationalization and localization projects within a client-side organization. They work closely with cross-functional teams, vendors, and stakeholders to ensure the successful delivery of localized products and services.
This role requires project management skills, a deep understanding of internationalization and localization processes, and linguistic quality assurance (LQA) expertise for day-to-day operations.
A localization solution architect works cross-functionally to understand a client's objectives and challenges, and then designs and implements a localization program based on advanced technology.
This role requires a comprehensive understanding of the translation technology landscape (e.g., translation management systems, machine translation engines, automated QA technology, and API integrations).
An international SEO specialist ensures that a multilingual website is visible in global markets. They conduct keyword research, optimize content, and work with localization teams to adapt material for different cultures and languages.
In addition to in-depth knowledge of SEO, this role requires in-depth knowledge of cultural differences, foreign language skills, marketing skills, and data analysis skills.
Vendor managers focus on developing relationships with third-party companies, contractors, and other external partners. They set up relationships with LSPs and individual professional linguists, and they may also develop partnerships with specialized individuals or companies to perform a wide range of localization functions the company requires but does not have the staff or talent in house to do on their own.
Vendor managers must be excellent at developing relationships. Depending on what the company needs, vendor managers will reach out and connect to companies and individuals all over the world in order to create networks of people and organizations that will serve their company’s specific needs when required. In order to choose the right partners, vendor managers will develop and follow a defined qualification process. This process may include doing screening and testing to assess skill levels, performing reference and background checks to ensure reliability, or reviewing credentials and past experience to verify areas of expertise.
Vendor managers will also set up and manage contract terms, and negotiate rates and fees. They will maintain a detailed database to keep track of all this information.
An operations manager focuses on management tasks. These tasks may include assigning resources and people to various projects, monitoring progress, giving feedback, and writing up performance reviews.
In positive situations where everything goes well, operations managers may offer promotions, benefits, and bonuses to employees as a reward. In cases where things don’t go so smoothly, the operations manager may craft employee development plans, design disciplinary procedures, and occasionally let people go who are not working out.
With the advent of artificial intelligence and large language models, we see new job roles emerging that focus on AI-based applications and integrations.
These roles depend on a combination of technological advancements, industry trends, and the evolving needs of language service companies and client-side organizations.
An AI-generated content reviewer/editor evaluates AI-generated content for accuracy, consistency, and adherence to ethical and stylistic guidelines. They ensure quality, context, and legality, and provide necessary editing and feedback to optimize AI-generated output for clarity and engagement.
This role requires writing, editing, and fact-checking skills, subject matter expertise, and a good understanding of how AI works.
A prompt engineer designs and formulates instructions (prompts) that guide AI models to achieve the desired output. They craft prompts to elicit specific responses, fine-tuning language, context, and tone to achieve accurate and contextually appropriate AI-generated content.
This role requires strong language skills, data analysis skills, and knowledge of AI technologies.
A linguistic data annotator/evaluator examines and labels linguistic elements within datasets to develop high-quality training data for language-based AI models. They identify and tag components such as syntax, semantics, entities, and sentiment, following specific guidelines to ensure consistent and accurate annotations. Their work is critical to refining natural language processing models to understand and generate human-like text.
This role requires linguistic training, programming skills, deep technology knowledge, and data analysis skills.