The ad says“Methodology: Proven track record using Agile, Scrum, or Kanban to manage complex workflows across a portfolio of programs”
The ad says“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”
The ad says“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”
The ad says“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”
The ad says“Experience with data annotation platforms (e.g., Label Studio, SuperAnnotate) and project management tooling (e.g., Jira)”
The ad says“Methodology: Proven track record using Agile, Scrum, or Kanban to manage complex workflows across a portfolio of programs”
The ad says“Experience with data annotation platforms (e.g., Label Studio, SuperAnnotate) and project management tooling (e.g., Jira)”
The ad says“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”
The ad says“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”
The ad says“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”
The ad says“Methodology: Proven track record using Agile, Scrum, or Kanban to manage complex workflows across a portfolio of programs”
The ad says“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”
The ad says“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”
The ad says“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”
The ad says“Experience with data annotation platforms (e.g., Label Studio, SuperAnnotate) and project management tooling (e.g., Jira)”
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
Benefits
Medical Benefits: Employees receive coverage of medical, dental, and vision insurance, plus FSA/DFSA, HSA, and Commuter benefits. In addition, LILT pays for basic life insurance, short-term disability, and long-term disability
Monthly lifestyle benefit stipend via the Fringe platform to allow employees to customize benefits to their lifestyle
Paid parental leave is provided after 6 months
Compensation: Meaningful equity, 401(k) matching, and flexible time off plus company holidays
Preferred experience
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
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
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
Communication: Writes clear, unambiguous guidelines and feedback for multilingual audiences and communicates status, risk, and tradeoffs crisply to leadership
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
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
