Industry · AI & Compute
Principal-Level Engineers for AI Accelerator and Custom Compute Programs
Building a custom NPU or AI inference chip is not a software problem with a hardware wrapper. It requires SoC architects who understand neural processing pipeline tradeoffs. How systolic array dimensions affect utilization on your workload, what the memory bandwidth requirements look like for your model sizes, where the bottleneck moves as you scale TOPS. It requires physical design engineers who have closed timing on complex dataflow architectures at 5nm or below, and verification engineers who can handle the scale of a billion-gate accelerator testbench.
We don't submit ML software engineers for silicon roles. We don't submit general-purpose chip engineers who've never touched an AI workload architecture. We source for the specific intersection of AI hardware knowledge and silicon execution experience. And we tell you exactly what each candidate brings before they walk into your interview.
Roles we place
AI and Compute Engineering Talent
SoC Architects / Chip Architects (AI)
NPU and TPU microarchitecture, systolic array and dataflow engine design, SIMD and vector processing pipelines, memory subsystem architecture for HBM and LPDDR integration, CXL and PCIe Gen5/6 host interface design. Principal-level; typically 15+ years, multi-tapeout experience.
RTL Design Engineers (AI Accelerator)
Custom neural processing engines in SystemVerilog, matrix multiplication accelerators, attention mechanism hardware, memory tile controllers, on-chip SRAM management, NoC (Network-on-Chip) fabric design.
Physical Design Engineers (Advanced Nodes / Chiplets)
5nm and 3nm physical design, chiplet architecture and die-to-die interface implementation, 2.5D/3D packaging with CoWoS and EMIB interposers, HBM integration, extreme routing congestion management on large compute tiles.
Design Verification Engineers (AI Scale)
Large-scale UVM testbench architecture for billion-gate designs, model-on-silicon accuracy correlation, coverage-driven verification of dataflow correctness, emulation environments for software-hardware co-validation.
DFT Engineers (AI SOC)
Full-chip DFT strategy for complex SoC designs, HBM BIST and DFT, hierarchical test architecture for large chiplet-based designs, tapeout signoff, ATE program development for production test.
Embedded / Systems Software Engineers (Inference Stack)
Model compiler development (operator lowering, kernel optimization, graph compilation), inference runtime development, device driver and kernel integration for custom accelerator PCIe cards, firmware for accelerator management and telemetry.
Why Game 7
What Makes AI Silicon Staffing Different
01
"AI Engineer" Is Not a Chip Engineer
The AI talent market conflates ML practitioners with silicon engineers. A principal SoC architect who has designed an NPU tapeout is a fundamentally different hire than a machine learning engineer who understands transformers. We screen for chip execution experience. Tapeout, process node, EDA toolchain, physical constraints. Not just familiarity with AI frameworks.
02
Advanced Node and Chiplet Experience Is the Constraint
Most AI accelerators are designed at 5nm or below and increasingly use chiplet architectures with 2.5D packaging. The pool of physical design engineers with tapeout experience at these nodes and with CoWoS/EMIB integration is thin. We've invested in knowing this community and can move on relevant candidates faster than a search starting from scratch.
03
CXL, HBM, And System-Level Integration Are Specialized
As AI accelerators move toward CXL memory pooling, HBM3/3e stacks, and multi-die architectures, the system-level integration knowledge becomes a distinct screening dimension. We ask about it explicitly: has this architect defined a CXL interface? Have they closed memory bandwidth requirements with HBM integration? These are not commoditized skills.
The screening standard
Technical depth
Domain expertise verified through structured discipline-specific screening
Domain match
Experience in your specific discipline, tools, and program phase
Active availability
Confirmed ready to start within your timeline
Rate alignment
Validated against your program budget before submission
Result: 2-4 verified candidates per role. No keyword-matched resumes. No noise.
Disciplines we staff for AI & Compute
Cross-Link to the Discipline Pages
FAQ
AI & Compute Staffing. Common Questions.
Does Game 7 Staffing place engineers for custom AI accelerator chip programs?+
Yes. Game 7 Staffing places principal-level engineers for custom AI accelerator and NPU/TPU programs, including SoC architects with neural processing microarchitecture experience, RTL design engineers for custom dataflow engines and systolic arrays, physical design engineers with advanced node (5nm, 3nm) and chiplet/2.5D packaging experience, and design verification engineers for large-scale accelerator testbenches. Game 7 screens for actual silicon execution experience. Tapeout history, process node, EDA toolchain. Not ML software background applied to hardware roles.
What is a chiplet architecture, and can Game 7 staff engineers with that experience?+
A chiplet architecture divides a large SoC into smaller, separately manufactured dies (chiplets) that are integrated on a 2.5D or 3D package using interposers (CoWoS, EMIB) or direct bonding. This approach allows companies to mix process nodes, improve yield on large designs, and reuse IP across product generations. AI accelerators from major companies are increasingly chiplet-based. Game 7 places physical design engineers with experience in die-to-die interface implementation, UCIe and proprietary die-to-die protocols, and 2.5D packaging constraints including bump pitch, power delivery across the interposer, and thermal management.
Can Game 7 Staffing place firmware engineers for AI accelerator bring-up and driver development?+
Yes. Game 7 places embedded and systems software engineers for AI accelerator bring-up, driver development, and inference runtime work. This includes device driver and Linux kernel module development for PCIe-attached accelerator cards, firmware for accelerator management controllers and telemetry, and model compiler or inference runtime development for custom silicon targets. These roles sit at the intersection of hardware and software. Candidates have both embedded system knowledge and familiarity with the AI software stack (ONNX, MLIR, or custom compiler backends).
Let's talk
Tell Us What You're Building in AI & Compute
Send us the program. We'll send a shortlist of 2-4 verified engineers within days.