About this role
Role Overview
The Manager, Production - Applied AI position at Enterprise AI Translation Platform (Lilt) is a full-time leadership role based out of the Boston office on a hybrid schedule. This opportunity suits an experienced operations professional with a strong background in AI/ML data production who is eager to lead high-performing project management teams in a distributed environment.
Key Responsibilities
Requirements
Nice to Have
Skills and Tools
Salary and Benefits
About the Company
Lilt is an enterprise AI translation platform on a mission to make the world's information accessible to everyone regardless of spoken language. Founded by former Google researchers who met working on Google Translate, the company combines cutting-edge AI, machine translation, and human-in-the-loop expertise. Lilt is trusted by hundreds of global enterprises and government organizations, and is backed by prominent investors including Sequoia, Intel Capital, and Redpoint.
How to Apply
To apply for this position, please visit the official company careers page on lilt.com.
Skills and tech stack
Questions candidates ask
What is the salary range for the Manager, Production - Applied AI role?
The salary range for this full-time position is between $110,000 and $145,000 USD per year.
Where is this job located?
This position is based in the Boston, MA office and operates in a hybrid work capacity.
What experience is required for this position?
Candidates must have 5+ years of experience in AI/ML data operations or production, including at least 2 years of direct management experience overseeing project managers or team leads in a distributed contractor environment.
What technical skills are needed?
Applicants should have proficiency in SQL, a strong understanding of Large Language Model training and evaluation processes, and experience using spreadsheets and performance dashboards.
Are there any preferred qualifications for applicants?
Preferred qualifications include fluency in multiple human languages, experience with multilingual data deliveries and rare-resource languages, familiarity with annotation platforms like Label Studio, and a background in data science, computer science, or ML engineering.
