AI System Engineer

About the role

This role defines and builds advanced memory architectures for AI infrastructure. The team drives hardware innovation to support high-performance, low-power computing. You will shape next-generation memory solutions while collaborating across hardware, software, and ecosystem partners.

Software engineers turn product ideas into working code. Engineers work in small teams, review each other's work, and ship in small batches. Most teams follow agile practices such as sprints and daily standups. Engineers also write tests, fix bugs, and improve performance. The field values clear communication as much as technical skill. Engineers spend part of every week on planning, code review, and debugging, not just writing new code. The ability to explain a technical decision in plain words separates strong engineers from the rest.

Key facts

What you'll do

Architect next-generation memory solutions and hardware prototypes that accelerate energy-efficient AI and machine learning workloads. Designs target high-performance computing applications and are validated through system modeling and performance analysis.

Translate system requirements into microarchitecture specifications and Register-Transfer Level (RTL) implementations. Logic design undergoes verification using lint, clock domain crossing checks, reset domain crossing analysis, power analysis, and timing closure to ensure quality digital intellectual property.

Hardware specifications, technical documentation, and test plans are developed and maintained to preserve design integrity and traceability.

Collaborate with system software engineers to achieve HW/SW co-optimization, aligning interfaces and protocols for efficient integration.

Requirements

Hold a Ph.D in Electrical and Computer Engineering or an MS with 4+ years of hands-on hardware design experience focused on SoCs and memory subsystems. Expertise in digital design includes microarchitecture specification development, system modeling, RTL logic design, synthesis, timing closure, and power-performance-area (PPA) analysis.

Demonstrate strong working knowledge and experience with FPGA development environments to prototype, validate, and iterate on designs efficiently. Strong communication and collaboration skills enable effective work with cross-functional teams on complex memory and storage challenges. Familiarity with memory and interface technologies such as DRAM, HBM, NVMe, CXL, and PCIe is a plus.

Practical notes

This position requires onsite presence in San Jose, California. Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

Hardware engineers specialize in digital design and verification within semiconductor development. Common tools include logic synthesis, timing analysis, and FPGA prototyping platforms. The field evolves through emerging memory interfaces and AI acceleration standards.

Questions to ask

Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.

Career growth

Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.