Research Scientist, Infrastructure Modeling and Reliability

**Summary:**

Meta builds technologies that help people connect, find communities, and grow businesses. Meta's infrastructure supports services used by billions of people, and operating that infrastructure efficiently requires increasingly sophisticated modeling of demand, utilization, reliability, and physical resource constraints.We are seeking an experienced Research Scientist or Applied Scientist to define and build new modeling approaches for power utilization across Meta's infrastructure. This role will lead the development of statistical and machine learning models that monitor power consumption, project peak demand, quantify uncertainty, and inform how Meta maximizes usable power within failure domains while maintaining target reliability levels. Qualified candidates will have has deep experience modeling high-dimensional, noisy, and interdependent systems, and has demonstrated the ability to translate scientific advances into production systems that influence large-scale infrastructure strategy.

**Required Skills:**

Research Scientist, Infrastructure Modeling and Reliability Responsibilities:

1. Define the scientific and technical strategy for modeling power consumption, peak risk, and reliability tradeoffs across large-scale infrastructure systems

2. Develop statistical, machine learning, and/or optimization models that forecast power demand, estimate peak distributions, quantify uncertainty, and support operational decision-making

3. Build approaches that reason about high-dimensional signals, correlated demand, failure-domain constraints, reserve margins, and reliability targets

4. Partner with engineering, capacity planning, data center, energy, hardware, operations, and finance teams to translate model outputs into infrastructure planning and utilization decisions

5. Establish evaluation frameworks, backtesting methods, confidence intervals, and monitoring systems to measure model quality and operational risk

6. Identify opportunities to safely increase power utilization, reduce stranded capacity, improve cost efficiency, and guide long-term infrastructure investment

7. Lead ambiguous, company-critical technical initiatives across organizations, influencing strategy and aligning stakeholders around scientifically grounded decisions

8. Mentor other scientists and engineers, raise the technical bar for modeling and forecasting systems, and represent Meta's work through appropriate external publications, talks, or industry engagement

**Minimum Qualifications:**

Minimum Qualifications:

9. 10+ years of experience developing statistical, machine learning, simulation, forecasting, optimization, or other quantitative modeling systems

10. Experience leading ambiguous, cross-functional technical programs from problem definition through model development, evaluation, deployment, and business impact

11. Experience coding in Python, R, C++, Java, or similar languages for data analysis, modeling, simulation, or production systems

12. Experience communicating complex technical concepts, assumptions, uncertainty, and tradeoffs to technical and non-technical audiences

13. Experience influencing technical strategy across multiple teams or organizations

14. Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience

**Preferred Qualifications:**

Preferred Qualifications:

15. Experience with infrastructure, capacity planning, power systems, energy systems, data centers, reliability engineering, distributed systems, supply-chain optimization, or resource allocation

16. Experience developing peak-demand forecasts, confidence intervals, risk estimates, anomaly detection, or backtesting frameworks

17. Experience mentoring senior technical contributors and building scientific communities across organizations

18. Demonstrated record of industry-level technical leadership, such as defining new research directions, influencing company strategy, publishing in leading venues, or shaping external technical standards

19. Experience building models that support operational decisions under explicit reliability, safety, cost, or utilization constraints

20. Experience applying optimization, operations research, or decision science to large-scale resource planning

21. Experience modeling high-dimensional, sparse, noisy, or strongly correlated data in production environments

22. Experience with time-series forecasting, probabilistic forecasting, Bayesian modeling, extreme-value modeling, causal inference, stochastic processes, simulation, or uncertainty quantification

**Public Compensation:**

$271,000/year to $347,000/year + bonus + equity + benefits

**Industry:** Internet

**Equal Opportunity:**

Meta is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Meta participates in the E-Verify program in certain locations, as required by law. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.

Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at accommodations-ext@meta.com.