Chip Design Engineer Jobs Near Me

AI Infrastructure Strategy & Analysis

• Support the VP of Technology Engineering & Innovation in evaluating emerging AI infrastructure technologies and future-ready data center strategies.
• Analyze AI workload characteristics including:

• Training vs. inference workloads
• GPU utilization patterns
• Dynamic workload fluctuations
• Rack-level power variability
• Networking and latency requirements

• Assess implications of AI workload behavior on infrastructure resiliency, scalability, efficiency, and operational design.
• Develop technical recommendations and infrastructure strategies supporting future AI deployments.

AI Compute & Chip Architecture Evaluation

• Analyze current and future AI compute platforms including NVIDIA GPU architectures, ARM-based platforms, custom AI accelerators and ASICs, optical networking and switching technologies, and emerging hyperscaler-designed AI chips.
• Evaluate implications of evolving chip architectures on rack density, power consumption, cooling requirements, electrical distribution, mechanical infrastructure, space planning, and future development standards.
• Model current and future AI rack power density trends including existing high-density deployments (50-120 kW), near-term AI deployments (150-300+ kW), and future ultra-dense AI cluster scenarios.
• Assess long-term impacts of emerging chip architectures on energy efficiency and future data center design and development standards.

Data Center Design, Development & Construction

• Support conceptual and detailed design efforts for AI-ready data center infrastructure.
• Assist in developing long-term infrastructure roadmaps for high-density AI deployments, liquid cooling adoption, modular infrastructure strategies, utility coordination, grid-parallel and microgrid solutions, and future AI campus development.
• Evaluate implications of AI infrastructure evolution on greenfield developments, existing facility retrofits, construction methodologies, scalability, and future campus master planning.
• Collaborate with engineering, development, and construction teams to develop scalable AI-ready infrastructure standards and deployment models.

Energy Strategy & Power Infrastructure

• Collaborate closely with the Energy Strategy Team to evaluate utility constraints, interconnection requirements, grid limitations, dynamic load fluctuation impacts, power quality and resiliency considerations, and onsite generation and distributed energy solutions.
• Support analysis of grid-parallel and islanded microgrid architectures, fuel cells, Battery Energy Storage Systems (BESS), bridge power solutions, natural gas generation, and renewable integration opportunities.
• Evaluate implications of AI workloads on substation development, transmission planning, utility coordination, and energy efficiency and PUE optimization.
• Assess how future AI compute growth will influence utility planning and power infrastructure strategies.

Cooling & Thermal Management Technologies

• Analyze current and emerging thermal management solutions including air cooling, direct-to-chip liquid cooling, immersion cooling, rear-door heat exchangers, and hybrid cooling architectures.
• Assess implications of ultra-high-density AI deployments on mechanical system design, water usage, cooling scalability, heat rejection strategies, thermal resiliency, and future cooling infrastructure standards.
• Evaluate cooling technologies and infrastructure requirements as AI rack densities continue to increase.

Vendor Engagement & Emerging Technology Assessment

• Interface directly with technology vendors, OEMs, utilities, and strategic partners across power generation, UPS systems, electrical infrastructure, cooling technologies, liquid cooling platforms, AI compute infrastructure, and networking and optical interconnect technologies.
• Lead technical assessments of next-generation technologies with respect to reliability, scalability, energy efficiency, sustainability, AI workload performance, construction complexity, and operational resiliency.
• Support proof-of-concept initiatives, pilot deployments, and technology benchmarking efforts.
• Develop executive-level recommendations regarding adoption of emerging AI infrastructure technologies and strategic engineering standards.

Cross-Functional Collaboration

• Collaborate with design engineering, construction, operations, energy strategy, procurement, utilities, technology partners, and external engineering firms and consultants.
• Support strategic planning initiatives and executive-level technical presentations.
• Assist in developing future infrastructure standards and innovation roadmaps for AI-enabled data center platforms.

Required

• Bachelor's degree in Electrical Engineering, Mechanical Engineering, Computer Engineering, Computer Science, Data Center Engineering, or a related technical discipline.
• 5+ years of experience in one or more of the following: data center infrastructure, AI/HPC infrastructure, power systems engineering, cooling technologies, or advanced infrastructure engineering.
• Strong understanding of emerging AI compute technologies and infrastructure implications.
• Ability to analyze complex technical systems and translate findings into actionable engineering and infrastructure strategies.

Preferred

• Master's degree and/or PhD in Engineering, Computer Science, Data Science, Energy Systems, or a related technical field.
• Experience with hyperscale or colocation data center environments.
• Knowledge of GPU infrastructure and AI workload behavior.
• Familiarity with utility coordination and energy systems.
• Understanding of high-density cooling technologies.
• Experience supporting large-scale infrastructure development projects.
• PE license or equivalent advanced technical credentials.