About Apertera

Apertera combines proprietary machine translation, LLM- and RAG-based systems, and a professional translation operation to deliver enterprise language solutions where accuracy and data sovereignty matter. Our PT technology strategy is built on automation, workflow orchestration, and data engineering, tightly integrated with the TMS and CAT infrastructure that professional translation depends on.

The Role

We are hiring a Senior / Localization Engineering Manager to build and lead a dedicated team whose sole mandate is to leverage Apertera's technology stack to raise the productivity, efficiency, and quality of our Professional Translation (PT) business. This is a people-management role: you will own the automation and tooling roadmap for PT, direct a cross-functional team, and be accountable for the internal systems and improvements they ship.
This is a technical leadership position embedded in the PT business. Because our PT work is largely high-leverage financial and securities translation, the biggest gains come not from machine translation but from three levers you will own: the quality of our linguistic assets (translation memories and terminology), workflow automation across the production lifecycle, and leverage of operational data beyond our core toolchain. You will turn operational friction into automated, auditable, well-integrated tooling.

What You'll Do

Team leadership

• Build, lead, and develop a cross-functional team spanning engineering, automation, and specialist linguistic-data and analytics skills; set direction, prioritize work, and grow the team's capability and composition as the function matures.
• Establish engineering practices, delivery cadence, and quality standards appropriate for tooling that supports a high-precision translation environment.

Automation & roadmap ownership

• Own and prioritize the roadmap for PT workflow automation and internal tooling, ensuring initiatives are practical, scalable, and tied to clear operational outcomes.
• Identify and deliver automation across the production lifecycle: intake and quoting, file analysis, project setup, assignment logic, vendor and resource workflows, QA tracking, and production reporting.
• Build automation and workflow orchestration for PT using tools such as n8n and Apache Airflow, connecting production systems into reliable, auditable pipelines consistent with client confidentiality requirements.

Linguistic-asset leverage

• Treat translation memories and terminology as core productivity assets: drive TM leverage, fuzzy-match optimization, and terminology consistency, which are the largest efficiency levers in high-repetition financial and securities content.
• Direct the specialist work of maintaining, segmenting, and improving these assets, and hold the team accountable for measurable gains in leverage and consistency.

Data leverage

• Unlock value from operational data beyond Phrase and Plunet by combining data from HubSpot, finance, QA ticketing, and the TMs into analytics that show where to focus and prove impact.

Systems & integration

• Improve data flow and integration across core production systems, including Plunet, Phrase, HubSpot, ATAI, and the Language QA ticketing process, to reduce duplicate entry, strengthen operational visibility, and support consistent handoffs.
• Partner with the broader Technology organization on architecture, security, and shared infrastructure so PT tooling fits the company's wider technical environment.

Partnership & delivery

• Work directly with the EVP of Professional Translation and LO leadership to surface workflow gaps, quality risks, and automation opportunities, and translate them into clear technical requirements and delivery plans.
• Ensure solutions are designed for real adoption by PMs, quoting, resources, linguists, revisors, DTP, QA, and finance, not just technical completeness.
• Define and track success metrics: improved TM leverage and terminology consistency, reduced manual work, improved consistency, stronger QA visibility, and faster turnaround.

Requirements

Required

• Team leadership. Experience leading or managing a technical team (engineers, automation, or solutions/localization engineering), including hiring, mentoring, and delivery ownership.
• Automation & data engineering. Hands-on background in automation, workflow orchestration, and data engineering, including building and operating pipelines with tools such as n8n and Apache Airflow, scripting, working with APIs, and connecting disparate systems.
• Language technology domain. Direct experience in the language technology ecosystem, such as a CAT/TMS environment (e.g., memoQ, Phrase, RWS/Trados, Smartcat), a machine translation or language AI platform, or a technical role within an LSP with hands-on involvement in translation workflows.
• Translation memory & terminology leverage. Strong understanding of how TM leverage, fuzzy matching, and terminology management drive productivity, enough to set direction, prioritize asset-quality work, and be accountable for measurable gains. Hands-on linguistic engineering is not required, but the judgment to direct it is.
• Translation workflow fluency. Working knowledge of professional translation workflows, including pre-translation, MTPE, revision, and QA.
• Integration & APIs. Practical experience designing and operating integrations between systems, including API design, integration patterns, and monitoring.
• Cross-functional delivery. Demonstrated ability to work across engineering, operations, and business stakeholders in an agile environment, and to translate operational needs into technical specifications.

 

Preferred

• Working familiarity with MT, LLM-based translation, or RAG, enough to integrate and orchestrate around these systems and make sound decisions; hands-on model development is not expected.
• Exposure to quality estimation, adaptive MT, or orchestrating LLM/RAG components within production workflows.
• Familiarity with DevOps practices (CI/CD, infrastructure-as-code) and localization engineering.
• Experience in regulated industries (legal, financial, government) where accuracy and data sovereignty are critical.
• Background in localization engineering, computational linguistics, or NLP.
• Experience in a high-growth or scale-up environment building processes alongside products.
• Bilingual or multilingual proficiency is a strong asset.