Industrial Engineer

Qualcomm•San Diego, CA

2d•$81,400 - $122,000•Onsite

About The Position

Own and improve lab infrastructure readiness and space utilization by maintaining an accurate power/cooling system of record, performing early feasibility and due diligence, and standardizing planning assumptions. Provide data-driven recommendations that help teams match lab demand to the right spaces and sequence upgrades effectively.

Requirements

Lab infrastructure engineering fundamentals (power distribution, UPS/generators, cooling capacity, airflow constraints, heat-load assumptions).
Capacity planning and modeling (demand vs. supply, diversity factors, growth scenarios, bottleneck analysis).
Data management and governance (system-of-record ownership, data quality checks, audit-ready documentation).
Standardization and repeatable frameworks (infrastructure profiles by lab type, scorecards, checklists, planning assumptions).
Cross-functional partnership (QREF/Facilities, IT/Networking, EHS, lab operations) to validate requirements and constructability.
Codes, standards, and safety mindset (electrical, fire/life safety, chemical and specialty-lab constraints as applicable).
Executive-ready communication (clear recommendations, risk escalation, and decision support for governance/leadership).
Bachelor’s degree in Industrial Engineering, Mechanical Engineering, Electrical Engineering, Facilities Engineering, Industrial Architecture or a related field (or equivalent practical experience).
5+ years of relevant experience in lab, facilities, critical environments, manufacturing, or infrastructure planning (design, operations, capacity planning, and/or project delivery).
Working knowledge of building electrical distribution and/or cooling concepts (heat-load estimation, airflow constraints, capacity vs. demand).
Experience performing site assessments/due diligence and translating findings into feasibility recommendations and documented risks.
Strong analytical and communication skills (Excel-based modeling, structured datasets, and cross-functional stakeholder communication).

Nice To Haves

Experience supporting R&D or specialty labs (e.g., high-density compute).
Proficiency with reporting/analytics tools (e.g. Power BI) and/or querying data (e.g., SQL); Python is a plus.
Experience applying AI/ML (including generative AI) for data quality, document extraction, forecasting/capacity analytics, or decision-support automation, with strong data governance and confidentiality practices.
Familiarity with layout tools (e.g., AutoCAD, Visio) and translating test-fits into requirements.
Experience developing trade studies and presenting recommendations to governance/leadership.
Professional Engineer (PE) license (Electrical/Mechanical) or Engineer-in-Training (EIT).
Project Management Professional (PMP) or equivalent project delivery certification.

Responsibilities

Maintain an audit-ready inventory of lab infrastructure attributes (power/cooling and key constraints), define data standards, and perform periodic accuracy checks.
Perform site walks and technical due diligence to validate capacity, identify constraints/risks, and document upgrade needs.
Develop and maintain repeatable infrastructure profiles and load templates by lab type to enable consistent estimates and comparisons.
Evaluate candidate spaces and develop option trade studies (reuse/upgrade/new build) with high-level cost/schedule/risk inputs; present decision-ready recommendations to planners and leadership.
Model demand vs. capacity, identify bottlenecks and optimization opportunities, and translate forecasted demand into roadmap triggers and sequencing recommendations.
Translate equipment/workflow needs into space requirements; define utilization standards and scorecards to compare spaces on readiness, flexibility, and time-to-ready.
Partner with Facilities/QREF, IT/Networking, EHS, and lab operations to validate requirements, align on implementation approaches, and ensure code/safety considerations are addressed.
Design and optimize engineering lab layouts, workflows, and material/equipment flows to improve productivity, safety, and utilization.
Conduct time studies, capacity modeling, and throughput analysis for lab operations and equipment usage.
Support lab deployment, reconfiguration, and scaling initiatives in collaboration with engineering, facilities, IT, and EHS teams.
Apply AI/ML techniques (e.g., predictive modeling, clustering, anomaly detection) to lab operations data such as utilization, demand forecasting, failure trends, and space planning.
Develop data-driven tools, dashboards, or decision models to support lab planning, asset lifecycle management, and infrastructure prioritization.
Partner with data science, IT, or platform teams to operationalize AI solutions within existing enterprise systems.
Lead Lean, Six Sigma, or similar process improvement initiatives across lab operations and support workflows.
Define standard methodologies, metrics, and KPIs for lab performance, efficiency, and scalability.
Support continuous improvement through root cause analysis, experimentation, and data-backed recommendations.
Serve as a technical liaison between engineering teams, lab operations, facilities, and leadership.
Translate complex operational data into clear insights, recommendations, and executive-ready materials.
Contribute to global lab strategy by identifying best practices, scalability opportunities, and automation candidates.