AI Data Quality Lead — Team & Delivery

Lever, Inc.

Polska

Hybrid

PLN 260,000 - 380,000

Full time

4 days ago

Be an early applicant

Application generator

An application made for this job — a tailored resume and cover letter that speak straight to the posting.

Get past ATS filters

Benefits offered by this job

Competitive compensation

Equity participation

Medical, dental, and vision coverage

Retirement benefits

Disability and life insurance

Job summary

Lever, Inc. is partnering to hire an Expert Team Lead, SWE in Poland. The role is hands-on leadership focused on building and maintaining high-quality AI data systems and scalable software engineering practices.

You will lead a team of AI Tutors and stay actively involved in code reviews, data quality, and process improvements. You will collaborate with data and engineering stakeholders to translate evolving model requirements into robust labeling strategies, evaluation workflows, and

Qualifications

Hands-on leadership with data labeling, AI training or evaluation, or related operational discipline.
Experience building scalable, high-performance software applications.
Understanding of data quality metrics, annotation workflows, and QA practices.
Knowledge of AI/ML concepts and their impact on model performance.
People management, coaching, and certification framework experience.
Experience leading small teams in data labeling or technical operations.
Ability to manage multiple projects in a fast-moving environment.
Strong analytical skills; KPI-driven improvements; SQL experience a plus.
Ability to work with distributed teams and mixed employment models.
Excellent written and verbal communication; ability to align stakeholders.
Strong organizational skills and proactive problem-solving mindset.
Ability to combine strategic thinking with hands-on execution.

Responsibilities

Own end-to-end quality and delivery for assigned data projects, reviewing work for accuracy and guideline adherence.
Lead, coach, and performance-manage AI Tutor teams with reviews and development plans.
Direct labeling, annotation, evaluation, and review activities to maintain high standards.
Establish QA processes, taxonomy standards, workflow guidelines, and evaluation criteria.
Monitor KPIs such as quality scores and throughput to improve efficiency.
Develop training materials, exercises, and certification benchmarks; manage certifications.
Collaborate with data managers, leads, and engineering to translate model requirements into labeling strategies.
Foster a high-performance culture with coaching and accountability.
Document outcomes and communicate status, risks, and results to stakeholders.
Represent AI Tutor needs while promoting collaboration across the organization.