Embedded Systems Engineer, High Speed DSP

In this role, you’ll work to shape the future of AI/ML hardware acceleration. You will have an opportunity to drive cutting-edge TPU (Tensor Processing Unit) technology that powers Google's most demanding AI/ML applications. You’ll be part of a team that pushes boundaries, developing custom silicon solutions that power the future of Google's TPU. You'll contribute to the innovation behind products loved by millions worldwide, and leverage your design and verification expertise to verify complex digital designs, with a specific focus on TPU architecture and its integration within AI/ML-driven systems.
In this role, for High-Speed DSP, you will architect and define the low-level firmware and control loops powering Google's physical-layer communications silicon. Sitting at the critical intersection of digital signal processing (DSP), hardware-software co-design, and real-time systems, you will lead the development of adaptive algorithms, error correction (FEC), and link-telemetry frameworks. Your work will directly bridge the gap between high-performance hardware pipelines and Google’s AI infrastructure, transforming complex communication systems theory into reliable, real-time silicon execution.
The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.
We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $192000 - $278000 (USD) + 20% bonus target + equity + benefits
Learn more about
benefits at Google
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Minimum qualifications
Bachelor’s degree in Electrical Engineering, Computer Engineering, a related technical field, or equivalent practical experience.
10 years of industry experience in embedded systems/firmware, communication systems engineering, or digital signal processing (DSP) development.
Experience developing embedded software (C/C++) for high-speed physical-layer interfaces (PHY), digital transceivers, or communication subsystems.

Preferred qualifications
Master's degree or PhD in Electrical Engineering, Computer Engineering, or a related technical field.
Expertise in Forward Error Correction (FEC) algorithms and advanced modulation schemes.
Track record defining firmware framework specifications, register maps, and Silicon Life-cycle Management telemetry for high-volume networking or AI silicon.
Background in wireless communications with deep domain knowledge of channel modeling, noise mitigation, and digital transceiver architectures.

Responsibilities
Define the long-term firmware roadmap and hardware-software partitioning for physical-layer interfaces, determining the optimal balance between hardware-fixed logic and programmable firmware.
Architect advanced "in-situ" monitoring systems and firmware-driven diagnostics to track physical-layer link health, Bit Error Rate (BER) trends, and channel quality across millions of ports in real-time.
Design, model, and implement bare-metal or RTOS firmware to control high-speed transceivers, managing real-time adaptive loops, clock and data recovery (CDR), and physical-layer calibration.
Partner with TPU Systems, Networking Software, and High-Performance Computing (HPC) teams to ensure silicon firmware integrates seamlessly with global cluster management and collective communication frameworks.
Identify and de-risk hardware-software interface bottlenecks (e.g., register maps, driver latencies, and boot-up sequencing) that impact Google's AI infrastructure.