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United States
Hybrid
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AI is changing how the world communicates — and LILT is leading that transformation.
We're on a mission to make the world's information accessible to everyone, regardless of the language they speak. We use cutting-edge AI, machine translation, and human-in-the-loop expertise to translate content faster, more accurately, and more cost-effectively without compromising on brand, voice, or quality.
At LILT, we empower our teammates with leading tools, global collaboration, and growth opportunities to do their best work. Our company virtues—Work together, win together; Find a way or make one; Dance in the customer's shoes; Quicker than they expect; Quality is Job 1—guide everything we do. We are trusted by Intel Corporation, Canva, the United States Department of Defense, the United States Air Force, ASICS, and hundreds of global Enterprises. Backed by Sequoia, Intel Capital, and Redpoint, we’re building a category-defining company in a $50B+ global translation market being redefined by AI.
LILT is seeking a Senior Product Manager, Translation Quality & Platform on our Product team to improve translation quality, the expert and linguist experience, and customer-facing analytics, while taking ownership of selected initiatives across the broader LILT platform. This person will combine strong product judgment, hands-on AI and agentic fluency, comfort with data, and customer-facing leadership.
At LILT, AI Review improves translations before human review, while AI QA helps experts catch high-impact errors before delivery. Our customer-facing analytics bring together quality metrics, turnaround-time reporting, and spend reporting. As LILT moves toward verified, provable translation, this role owns the product requirements for quality measurement and evidence: how we evaluate improvements, how quality signals inform delivery decisions, and how we communicate results and limitations to customers.
This is a permanent, full-time role that begins by taking on an existing PM's translation quality, expert-experience, and analytics work during leave, then evolves with business priorities and team capacity.
The PM will serve as the single directly responsible individual for AI Review, AI QA, and the expert and linguist experience. They will own customer reporting needs, reporting experiences, and product prioritization for customer-facing analytics, partnering with Data Engineering on the underlying data capabilities. They will use AI and agentic systems to accelerate product discovery, prompt and model evaluation, data analysis, and cross-functional execution while working closely with Engineering, Applied AI and Research, Production, Quality, Customer Engineering, Customer Success, Forward-Deployed Engineering, and customers.
As part of a small, dynamic Product team, this person will serve as a product point of contact for customers and take on selected broader Platform initiatives, such as workflow automation, admin settings, permissions, or account structures. Scope and priorities will be set with Product leadership and the relevant product owners, balancing customer needs, business priorities, and team capacity.
Roadmap and priorities: own the roadmap, tradeoffs, and delivery sequencing for translation quality, the expert and linguist experience, and customer-facing reporting experiences. Align analytics delivery with Data Engineering on data readiness and capacity. Keep intake, backlog, status, risks, and customer impact clear and current.
AI Review and AI QA: lead product delivery from problem definition through evaluation, requirements, user acceptance testing (UAT), and release. Improve translation quality and consistency, including adherence to customer terminology and translation requirements. Set appropriate quality, latency, cost, and reviewer-effort targets for each feature, and use evidence to guide release decisions. Engineering owns implementation and reliability; Applied AI and Research own technical model, prompt, and evaluation work.
Quality measurement and trust: define product evaluation questions and acceptance criteria, partnering with Research, Applied AI, and Quality on benchmarks and sampled human evaluations. Translate results into product decisions and clear customer-facing evidence, including support for new AI verification workflows and understandable quality-based routing decisions.
Customer-facing analytics: own the product roadmap and reporting experience that help customers understand translation quality, turnaround time, spend, and usage. Translate customer needs into clear product requirements and intuitive reporting experiences, and define metrics and acceptance criteria with Engineering and Data Engineering. Partner on delivery and validate that reporting is accurate, understandable, and useful; Engineering and Data Engineering own the underlying pipelines and infrastructure.
Expert and linguist experience: own product improvements to the tools and experiences experts use to review, assess, and deliver translations. Partner with Production, Quality, linguists, and Engineering to reduce friction, improve usability, and enable effective human–AI collaboration. Prioritize workflow automation and platform improvements that make expert review and delivery more efficient.
Customer discovery and product triage: lead technical conversations on quality, analytics, and the expert and linguist experience, and act as a product point of contact for customers. Customer Engineering and Customer Success own routine support; you partner on product escalations and coordinate legal, security, or data-processing questions with the appropriate teams.
Delivery and communication: write clear product requirements documents (PRDs), acceptance criteria, and UAT plans; lead UAT and release readiness with cross-functional partners; and communicate decisions, progress, and release notes.
Broader Platform work: take ownership of selected additional initiatives as priorities and capacity allow, with scope agreed with Product leadership and the relevant product owners.
Connector strategy, delivery, and reliability are outside this role's scope and sit with the Technical Product Manager, Connectors.
Hands-on builder mindset and AI fluency: Demonstrated ability to use AI and agentic workflows to accelerate discovery, evaluation, analysis, requirements, documentation, and troubleshooting. Writes and tests prompts, runs analyses, and builds lightweight prototypes or tools when useful. Evaluates outputs critically without outsourcing judgment or accountability; production implementation remains with Engineering.
AI evaluation and LLM product experience: Understands how to measure an AI feature using test sets, precision and recall, acceptance rates, latency, cost, and error scoring. Can distinguish a real quality gain from noise and explain tradeoffs across quality, speed, reviewer effort, and economics to non-technical stakeholders.
Data and analytics fluency: Comfortable with SQL or similar tools, defining metrics, and turning customer reporting needs into clear data requirements.
Production and quality grounding: Understands how translation is actually produced: linguist workflows, review stages, quality programs, and where they break. Uses that knowledge to design AI features that production teams and linguists trust and adopt.
Product judgment and ownership: Can prioritize across a noisy, multi-customer backlog; write clear specifications and acceptance criteria; sequence work against capacity; distinguish one-off issues from systemic problems; and drive ambiguous work to closure. Learns unfamiliar product areas quickly and takes ownership of selected initiatives beyond an established specialty.
Customer presence: Can lead discovery and troubleshooting directly with enterprise customers while translating complex details for localization managers, administrators, and business stakeholders. This is an essential requirement.
Clear communication: Produces concise decisions, plans, status updates, documentation, and release notes across technical and non-technical teams.
Background in localization production, operations, or language quality before moving into product, including structured linguistic quality evaluation programs, such as Multidimensional Quality Metrics (MQM), and terminology management.
Experience with embedded analytics or BI tools in a B2B SaaS product.
Experience supporting AI features in high-assurance or regulated environments, including subprocessor and data-processing requirements.
Experience with platform product work, such as admin settings, user permissions, APIs, or account structures.
Experience introducing AI-enabled operating practices across product and engineering teams, with measurable improvements in speed or quality.
Maintain continuity of active work and customer relationships while building a strong understanding of the portfolio, customer needs, and the expert and linguist experience.
Keep priorities, backlog, status, and risks clear and current, with scope and delivery expectations aligned across teams.
Drive measurable improvements in AI Review, AI QA, and expert review effectiveness, using automated quality assessment, evaluation evidence, and customer and linguist feedback to guide decisions.
Ensure quality claims within the portfolio are backed by credible evaluation evidence, with results and limitations clear to customers.
Advance customer-facing analytics toward clear, trustworthy reporting on quality, turnaround time, and spend, aligned with data readiness and engineering capacity.
Turn expert and linguist pain points into practical improvements in usability, reviewer effort, and delivery efficiency, in partnership with Production, Quality, and Engineering.
Partner effectively with customer-facing teams on timely product triage and clear follow-through.
Use AI and agentic workflows to improve the speed and quality of product work, while adapting to additional priorities, including selected Platform work.
This role is open to candidates based in the United States or the United Kingdom. We are unable to offer visa sponsorship for this position.
Our founders, Spence and John met at Google working on Google Translate. As researchers at Stanford and Berkeley, they both worked on language technology to make information accessible to everyone. While together at Google, they were amazed to learn that Google Translate wasn’t used for enterprise products and services inside the company.The quality just wasn’t there. So they set out to build something better. LILT was born.
LILT has been a machine learning company since its founding in 2015. At the time, machine translation didn’t meet the quality standard for enterprise translations, so LILT assembled a cutting-edge research team tasked with closing that gap. While meeting customer demand for translation services, LILT has prioritized investments in Large Language Models, human-in-the-loop systems, and now agentic AI.
With AI innovation accelerating and enterprise demand growing, the next phase of LILT’s journey is just beginning.
What sets our platform apart:
Brand-aware AI that learns your voice, tone, and terminology to ensure every translation is accurate and consistent
Agentic AI workflows that automate the entire translation process from content ingestion to quality review to publishing
100+ native integrations with systems like Adobe Experience Manager, Webflow, Salesforce, GitHub, and Google Drive to simplify content translation
Human-in-the-loop reviews via our global network of professional linguists, for high-impact content that requires expert review
Featured in The Software Report’s Top 100 Software Companies!
LILT makes it onto the Inc. 5000 List.
LILT’s continues to be an intellectual powerhouse, holding numerous patents that help power the most efficient and sophisticated AI and language models in the industry.
Check out all our news on our website.
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At LILT, we are committed to a fair, inclusive, and transparent hiring process. As part of our recruitment efforts, we may use artificial intelligence (AI) and automated tools to assist in the evaluation of applications, including résumé screening, assessment scoring, and interview analysis. These tools are designed to support human decision-making and help us identify qualified candidates efficiently and objectively. All final hiring decisions are made by people. If you have any concerns, require accommodations, or would like to opt-out of the use of AI in our hiring process, please let us know at recruiting@lilt.com.
LILT is an equal opportunity employer. We extend equal opportunity to all individuals without regard to an individual’s race, religion, color, national origin, ancestry, sex, sexual orientation, gender identity, age, physical or mental disability, medical condition, genetic characteristics, veteran or marital status, pregnancy, or any other classification protected by applicable local, state or federal laws. We are committed to the principles of fair employment and the elimination of all discriminatory practices.
