About LILT

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.

Key Responsibilities

• PM Performance

  • Outcome: Every active program has accountable Project Manager(s), and every PM carries a workload within the agreed span. Programs hit on-time delivery and first-pass acceptance targets without escalation; each PM is reviewed monthly against a scorecard of throughput, quality, and cost-per-task.

• PM Hiring, Onboarding, and Development

  • Outcome: PM pool capacity keeps pace with signed demand: new PMs are sourced, onboarded, and running their first program within the agreed ramp window, all programs start on time, and PM attrition is below threshold.

• Quality Interventions Across Programs

  • Outcome: Quality dips are caught mid-program through QA loops and corrected via retraining of annotator pools or guideline updates. Repeated misses by a PM or annotator pool lead to documented remediation or replacement. Issues are proactively discovered.

• Process Standardization & Software Improvements

  • Outcome: Programs launch from shared playbooks, guideline templates, and dashboard standards rather than being rebuilt per engagement. Every post-mortem produces documented improvements that lead directly into our custom software stack, and time from program handoff to first delivery declines quarter over quarter.

• Escalation and Cross-Functional Interface

  • Outcome: Risks surface to Technical Program Managers early enough to be managed. Escalations between the PM pool, Quality, Talent, and Delivery are resolved within agreed timelines.

Qualifications

• People management in AI data operations: 5+ years in AI/ML data operations or production, including 2+ years directly managing project managers or team leads in a distributed, multi-time-zone contractor environment.
• LLM knowledge: Strong understanding of LLM training processes (pre-training, SFT, RLHF) and evaluation methodologies (human-in-the-loop, red teaming), and of what drives quality and throughput in annotation workflows.
• KPI-driven management: Has run teams against throughput, quality (accuracy, IAA, gold-set), and cost-per-task targets; advanced proficiency with spreadsheets and dashboards, and able to use SQL to extract and analyze performance data.
• Delivery track record: Has sustained on-time delivery and acceptance targets across multiple concurrent data collection or evaluation programs for enterprise or research lab customers.
• Contractor workforce operations: Has hired, ramped, performance-managed, and offboarded hourly and freelance staff across regions and languages.
• Methodology: Proven track record using Agile, Scrum, or Kanban to manage complex workflows across a portfolio of programs.
• Communication: Writes clear, unambiguous guidelines and feedback for multilingual audiences and communicates status, risk, and tradeoffs crisply to leadership.

Preferred Skills

• Fluency in multiple human languages.
• Experience with multilingual data deliveries (pre-training, SFT, RLHF, machine translation, multimodal, etc.), especially in rare-resource languages
• Experience with data annotation platforms (e.g., Label Studio, SuperAnnotate) and project management tooling (e.g., Jira).
• Background in ML engineering, computer science, or data science.

Our Story

Our founders, Spence and John met at Google working on Google Translate. As researchers at Stanford and Berkeley, they both work…