In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Sunnyvale, CA, USA; Kirkland, WA, USA; New York, NY, USA.
US: $163000 - $236000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.
- Health, dental, vision, life, disability insurance
- Retirement Benefits: 401(k) with company match
- Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment
- Sick Time: 40 hours/year (increased to 69 hours/year for Seattle) including 5 discretionary sick days per instance
- Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks
- Baby Bonding Leave: 18 weeks
- Holidays: 13 paid days per year
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Sunnyvale, CA, USA; Kirkland, WA, USA; New York, NY, USA.
Minimum qualifications:
- Bachelor's degree in Learning Systems and Evaluation, Data Science, Cognitive Science (with a focus on quantitative performance modeling), Computer Science, or equivalent practical experience.
- 5 years of experience in technical enablement program management, learning systems engineering, or technical validation.
- Experience managing end-to-end data pipelines for competency frameworks, technical telemetry systems, or analytics.
- Experience applying structured testing methods or data-driven evaluations to technical training pipelines or software platforms to verify learning efficacy.
Preferred qualifications:
- Advanced degree (Master's or Ph.D.) in Learning psychology, Cognitive science, or Data science with a focus on quantitative validation and evaluation architectures.
- Experience in psychometric validation and evaluation architectures used to guarantee the integrity and reliability of technical capability benchmarks.
- Experience in applying advanced behavioral data modeling or cognitive load frameworks to software platform interactions and technical competency definitions.
- Practical familiarity with cloud infrastructure environments and the integration of AI tools into technical enablement pipelines.
About the job
A problem isn’t truly solved until it’s solved for all. That’s why Googlers build products that help create opportunities for everyone, whether down the street or across the globe. As a Technical Program Manager at Google, you’ll use your technical expertise to lead complex, multi-disciplinary projects from start to finish. You’ll work with stakeholders to plan requirements, identify risks, manage project schedules, and communicate clearly with cross-functional partners across the company. You're equally comfortable explaining your team's analyses and recommendations to executives as you are discussing the technical tradeoffs in product development with engineers.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $163000 - $236000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Lead the programmatic scoping and scaling of technical enablement telemetry and learning evaluation, data footprints, and automated capability analytics.
- Architect and manage robust quantitative engineering validation data pipelines to measure knowledge transfer efficacy across the workforce.
- Define data environment psychological constructs and cognitive load metrics, establishing precise system health goals with engineering leads. Translate proficiency telemetry into concrete feature requests, system schemas, and technical specifications for enablement platform developers.
- Own end-to-end instrumentation design for technical evaluations and automated data collection pipelines. Oversee system experimentation frameworks to evaluate enablement infrastructure efficacy and knowledge transfer models.
- Drive cross-functional alignments with technical leads to implement the AI Competency matrix and proficiency validation models globally. Establish data quality gates and validation models to guarantee the integrity and reliability of technical platform metrics.
