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Technical Lead, Custom Digital Circuit Design, Mixed Signal
About the job
In this role, youll 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 our most demanding AI/ML applications. Youll be part of a team that pushes boundaries, developing custom silicon solutions that power the future of our 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.
As the Lead Architect for our custom back-end solutions for high-speed internal connections, you will set the design standards for our custom silicon. You will ensure our physical design methods can support data speeds of 1.6T and above while maintaining high manufacturing reliability and performance. You will create the design framework for our future hardware, solving complex engineering challenges in the latest manufacturing processes.
Responsibilities
Define the global back-end and custom Physical Design methodology, driving the transition to automated Analog and Mixed-Signal (AMS) flows and "Layout-Aware" design.
Lead full custom digital circuit design at the transistor level optimized for low power.
Evaluate and de-risk new process features in GAA (Gate-All-Around) nodes (e.g., Backside Power Delivery, Buried Power Rails) for high-speed analog use cases.
Drive the long-term Roadmap for Area and Power density, ensuring our PHYs remain the most efficient in the industry.
Act as the primary liaison with foundries to optimize Design for Manufacturing (DFM) and maximize yield for massive TPU-scale chips.

Required Senior Design Engineer, Edge AI, Cloud
About the job
Be part of a team that pushes boundaries, developing custom silicon solutions that power the future of our direct-to-consumer products. You'll contribute to the innovation behind products loved by millions worldwide. Your expertise will shape the next generation of hardware experiences, delivering unparalleled performance, efficiency, and integration.
In this role, youll work to shape the future of an Edge-AI product, bringing high-performance intelligence to the edge. You will have an opportunity to drive distributed inference technology that powers real-time systems where latency and reliability are mission-critical.
Youll be part of a team that pushes boundaries, developing robust, autonomous solutions that define the next generation of intelligent infrastructure and hardware for the edge. You'll contribute to the innovation behind products that transform industries, leveraging your expertise in system-level integration and localized processing to deploy complex AI models across sophisticated hardware platforms, ensuring intelligence is embedded exactly where the action happens.
Behind everything our users see online is the architecture built by the Technical Infrastructure team to keep it running. From developing and maintaining our data centers to building the next generation of our platforms, we make our product portfolio possible. We're proud to be our engineers' engineers and love voiding warranties by taking things apart so we can rebuild them. We keep our networks up and running, ensuring our users have the best and fastest experience possible.
Responsibilities
Lead a complex ASIC subsystem.
Understand how it interacts with software and other ASIC subsystems to implement groundbreaking data center.
Define high-performance hardware/software interfaces. Write micro architecture and design specifications.
Define efficient micro-architecture and block partitioning/interfaces and flows.
Collaborate closely with software, verification, and physical design stakeholders to ensure the designs are complete, correct, and performant.

Required Silicon Validation and Debug Engineer
About the job
Be part of a team that pushes boundaries, developing custom silicon solutions that power the future of our direct-to-consumer products. You'll contribute to the innovation behind products loved by millions worldwide. Your expertise will shape the next generation of hardware experiences, delivering unparalleled performance, efficiency, and integration.
We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Clouds Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.
Responsibilities
Contribute to developing and improving post-silicon test content that exercises both IP and SoC levels workloads and other compute-intensive scenarios.
Collaborate with architecture, design, and firmware teams to define comprehensive validation plans for SoC features, focusing on power consumption, thermal management, sensors behavior, and performance metrics.
Work closely with design and firmware teams to propose and implement solutions for optimizing SoC power efficiency and performance, including tuning core-to-memory latencies and bandwidth, power control loops, thermal control loops, etc.
Develop and maintain automated test scripts and frameworks (primarily in Python) to improve validation efficiency and coverage.
Participate in early silicon bring-up and platform bring-up activities, ensuring the stability and functionality of high-power multi-core SoCs.

Required SoC Test Engineer, Cloud
About the job
Be part of a team that pushes boundaries, developing custom silicon solutions that power the future of our direct-to-consumer products. You'll contribute to the innovation behind products loved by millions worldwide. Your expertise will shape the next generation of hardware experiences, delivering unparalleled performance, efficiency, and integration.
Responsibilities
Collaborate with Architecture, Design and Verification teams to develop new product bring-up, validation, characterization, and qualification strategies and manufacturing test solutions for new High Performance Computing (HPC) products in advanced process technologies.
Verify test solutions on pre-silicon models (simulation or emulation) and develop ATE test modules and binning flows.
Develop and validate test programs on Automated Test Equipment (ATE) platforms for new product integration (NPI) in preparation for high volume manufacturing (HVM), working with ATE vendors and internal cross-functional teams.
Product sustain support, including volume data analysis, test time and yield improvements, assess test escapees and Return Merchandise Authorization (RMAs), localize failures, implement containment measures and partner with design, manufacturing, quality and reliability teams to root cause and implement corrective actions.
Develop tools, flows and methodologies to continuously improve and automate the testing life cycle.

Required Design Verification Engineer, Networking, Cloud
About the job
Be part of a team that pushes boundaries, developing custom silicon solutions that power the future of our direct-to-consumer products. You'll contribute to the innovation behind products loved by millions worldwide. Your expertise will shape the next generation of hardware experiences, delivering unparalleled performance, efficiency, and integration.
As a Design Verification Engineer, you will be a part of the Research and Development team to verify digital designs, develop constrained-random test environments and drive system testing to closure. You will collaborate with design and verification teams, manage the verification life-cycle and uncover bugs through corner-case testing.
Responsibilities
Plan and execute the verification of digital design blocks by understanding specifications and working with design engineers to define key verification scenarios.
Develop and refine random verification environments using SystemVerilog/Universal Verification Methodology (UVM) or Specman to ensure effective test coverage.
Define and implement various coverage measures to capture stimulus and corner-case scenarios.
Collaborate with design engineers to debug tests and ensure functional correctness of design blocks.
Drive coverage analysis to identify verification gaps and demonstrate progress towards tape-out.

Required Staff Embedded Firmware Engineer, High Speed DSP
About the job
In this role, youll 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 our most demanding AI/ML applications. Youll be part of a team that pushes boundaries, developing custom silicon solutions that power the future of our 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.
As a Staff Embedded Firmware Engineer for High-Speed DSP, you will architect and define the low-level firmware and control loops powering the world's most advanced 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 our planetary-scale AI infrastructure, transforming complex communication systems theory into reliable, real-time silicon execution.
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 firmwarm.
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 our AI infrastructure.

we are looking for a Research Scientist - Sovereign AI Research.
We are hiring across the following specializations. We expect depth in at least one area and the intellectual range to collaborate across them.
Computer Vision: object detection, segmentation, multimodal grounding, vision-language models, contrastive and self-supervised representation learning, low-resource and few-shot visual recognition.
NLP / Speech: LLMs, NERs, relation extraction, span-based and generative IE, semantic textual similarity, multilingual and cross-lingual transfer.
Reinforcement Learning: MDPs, POMDPs, model-based and model-free RL, Online Offline methods, reward modeling, sim-to-real transfer, compute-aware planning.
Graph Learning: GNNs, graph clustering, community structure, generative methods, knowledge graph embeddings, dense and sparse semantic retrieval.
Optimization: convex and nonconvex optimization, constrained and Lagrangian methods, combinatorial and integer programming, knowledge distillation (response, feature, and relation-based), test-time optimization, Bayesian optimization, resource-aware inference.
Representation Learning: contrastive learning, self-supervised and unsupervised pre-training, disentangled representations, metric learning and embedding spaces, cross-modal and multimodal alignment, meta learning (hypernetworks), transfer learning and domain adaptation, probing and interpretability of learned representations, world models.
Neurosymbolic AI: neuro-symbolic integration, differentiable theorem proving, inductive logic programming (ILP), probabilistic soft logic (PSL), causal inference and structural causal models (SCMs), programmatic and compositional reasoning
Responsibilities:
Define and execute a research within your track, experiments, quality gates, and upper bounds in rigorous manner.
Collaborate across tracks on system integration and cross-disciplinary research.
Collaborate work with engineering teams until production.
Mentor junior researchers and contribute to a culture of technical excellence

we are looking for a Applied AI Researcher.
The Dream-Maker Responsibilities
Open Research Tracks
Familiarity with at least one is expected:
Computer Vision: object detection, segmentation, multimodal grounding, vision-language models, contrastive and self-supervised representation learning, low-resource and few-shot visual recognition.
NLP / Speech: LLMs, NERs, relation extraction, span-based and generative IE, semantic textual similarity, multilingual and cross-lingual transfer.
Reinforcement Learning: MDPs, POMDPs, model-based and model-free RL, Online Offline methods, reward modeling, sim-to-real transfer, compute-aware planning.
Graph Learning: GNNs, graph clustering, community structure, generative methods, knowledge graph embeddings, dense and sparse semantic retrieval.
Optimization: convex and nonconvex optimization, constrained and Lagrangian methods, combinatorial and integer programming, knowledge distillation (response, feature, and relation-based), test-time optimization, Bayesian optimization, resource-aware inference.
Representation Learning: contrastive learning, self-supervised and unsupervised pre-training, disentangled representations, metric learning and embedding spaces, cross-modal and multimodal alignment, meta learning (hypernetworks), transfer learning and domain adaptation, probing and interpretability of learned representations, world models.
Neurosymbolic AI: neuro-symbolic integration, differentiable theorem proving, inductive logic programming (ILP), probabilistic soft logic (PSL), causal inference and structural causal models (SCMs), programmatic and compositional reasoning
Responsibilities:
Train and evaluate models across research tracks, iterating fast while documenting rigorously.
Build and maintain benchmarking pipelines and evaluation suites.
Curate, structure, and preprocess datasets; contribute to synthetic data generation workflows.
Run ablations and controlled experiments to support research hypotheses.
Reproduce and stress-test results from recent literature relevant to the group's work.
Collaborate across tracks and with engineering teams through to production handoff.

ISI is looking for an experienced Embedded Software Engineer to join our software team. You’ll be the single owner of the software stack on one of our satellite programs - ground station through flight computer to payloads. The code, the builds, the CI, and the system integration are yours. You’ll work alongside hardware engineers during system bring-up and the wider software team day-to-day. We’re looking for someone who reads documentation and ships independently.

Responsibilities:

* Own the software stack across ground station, flight computer, and payloads
* Maintain Yocto-based payload software: build, package, deploy
* Write and maintain the embedded and host-side software
*  Integrate code contributions from adjacent teams and turn them into shippable software
* Partner with hardware engineers on system bring-up and integration
* Debug across hardware, kernel, and userspace
*  Write and maintain automated tests and technical documentation

Advantages:

* Background in defense or other embedded-system-of-systems environments
* Experience with constrained or resource-limited Linux platforms
* Docker / containerization experience
* Familiarity with embedded interconnect protocols (I2C, SPI, UART, RS422/485) and integrating drivers that use them

we are looking for a AI Applied Scientist - Insights & Recommendations.
As AI Applied Scientist, youll be at the intersection of advanced technology and meaningful impact with clear business cases. You will have access to real-time (RT) big data from multiple Sensors & Data sources, and a chance to discover, explore research, and develop cutting-edge models that directly impact industrial manufacturers around the globe. As part of the Applied AI/ML Science team , you will have the opportunity to continuously grow and learn while building and deploying advanced models directly into our products, and stay at the forefront of the world's technologies, witnessing firsthand their impact on our customers.
A Day In Your Life:
Own the algorithm lifecycle from problem definition and data analysis to prototyping and delivering production-ready models.
Combine classic methods with inference techniques: statistics, Time series, Deep learning, anomaly detection, Recommendation Systems, Transformers, and Inference to extract data-driven insights.
Research, design, and build Agentic applications on top of sensor time-series data, textual data, images, ML applications, and other various data sources.
Engage with customers and collaborate with our product team to develop innovative solutions utilizing new types of data.
Leverage modern technologies: work with cloud-based big data platforms for storage, distributed processing, LLMs and agents (GPT, Claude, Gemini), defining & building Gurdrails, reasoning chain as function call, planning, ML-based vectorization and embeddings, and stream analysis.