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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 on a mission to secure the AI transformation that every company is going through, building a platform that protects organizations as they adopt AI tools at scale. This is your chance to join one of the fastest-growing areas in our company and develop technology that will define how cybersecurity operates in the AI era. You will be part of a multidisciplinary team of backend engineers, endpoint developers, AI researchers, and product experts from Israel and global acquisitions, all working together to deliver cutting-edge, end-to-end security solutions to the worlds largest enterprises.
As a Software Developer on our team, you will work across both backend services and endpoint components, designing scalable systems that connect endpoint-level data collection with cloud-based analysis and enforcement. Youll play a key role in building the agents and services that monitor and control interactions with AI tools, shaping a new category of security products that operate seamlessly across platforms and environments.
Key Responsibilities
Develop and maintain backend services alongside endpoint components for a cross-platform security platform
Design scalable systems that integrate endpoint-collected data with cloud-based processing and enforcement
Build and enhance endpoint agents responsible for monitoring and controlling interactions with AI tools (e.g., coding assistants, web-based AI services)
Implement data pipelines, policy enforcement logic, and secure data handling mechanisms across endpoint and backend layers
Collaborate with AI and product teams to deliver end-to-end solutions
Troubleshoot and resolve complex issues across distributed system and endpoint components.

Were looking for a hands-on Senior Automation Engineer to join our QA Automation team and play a key role in shaping the quality, scalability, and reliability of SaaS cyber platform.
In this role, youll own and evolve automation strategy, design robust testing systems, and influence engineering practices across teams. Youll work on complex cloud, security, and data-driven flows, while using AI tools as part of your daily workflow to accelerate development, improve test coverage, investigate failures, and increase automation efficiency.
The Responsibilities:
Lead the design, development, and maintenance of automated tests across UI, API, and integration layers.
Collaborate closely with developers and QA engineers to define robust test strategies and drive quality from the ground up.
Build and evolve scalable, maintainable test frameworks that empower fast and confident delivery.
Integrate automation into CI/CD pipelines, maintaining high coverage across services and components.
Participate in feature planning to align test efforts early and ensure full lifecycle coverage.
Leverage infrastructure-as-code and scripting to streamline and optimize testing environments.

We are seeking a Senior AI Engineer to join core product organization, where you will design, build, and scale next-generation AI systems powering real-world cybersecurity use cases across our diverse product portfolio (Posture, Detection, and CTI). This role focuses on developing production-grade systems leveraging LLMs, advanced machine learning, and agent-based architectures.
You will join a team within Cyber R&D organization-leading the companys core product portfolio- while driving AI innovation and establishing engineering best practices across the domain. The team focuses on building and optimizing large-scale AI systems, including LLM-based solutions and advanced multi-agent workflows, working closely with data scientists and researchers to bring ideas into production.
The Responsibilities:
Design, build, and own end-to-end AI solutions- from data collection and preprocessing to model training, evaluation, and production deployment.
Optimize systems for performance, scalability, and reliability in production environments.
Collaborate closely with product, design, and engineering teams to identify and deliver AI-driven capabilities that address real customer needs.
Stay up to date with emerging AI/ML technologies, frameworks, and best practices, and apply them where they create real impact.
Work across the stack, contributing to backend systems and data pipelines that support large-scale AI applications.
Troubleshoot and resolve complex system issues, including performance bottlenecks, race conditions, and memory-related challenges.
Approach problems with a strong analytical mindset, delivering robust solutions while contributing to a high-performing, collaborative team environment.

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 a rapidly growing, award-winning B2B tech unicorn with an ambitious mission to revolutionize the payroll & payments industry. With over $400M raised from multiple tier-one investors, our innovative technology provides a comprehensive solution for managing global workforces, encompassing everything from hiring and onboarding to managing and paying employees in over 160 countries.
We're building AI to tackle one of the hardest operational challenges in business: getting workforce management right across many markets, each with its own tax rules, labor laws, payment rails, and constantly shifting policies.
In this role on our AI team, you'll focus on agentic workflow automation. You'll design, build, and deploy reliable AI agents that automate complex HR, payroll, and payment workflows, handling decision-making, document intelligence, and complex tasks that typically require human judgment.
What You'll Do
Design and build agent-based systems that automate document processing, business insights, and complex enterprise workflows
Build observability, evaluation, and feedback loops for agent behavior to improve reliability, accuracy, and trust in production
Own the technical architecture and engineering standards for agentic systems
Build or manage data pipelines to process large volumes of documents and unstructured data
Collaborate with domain experts to identify high-value automation targets and deliver end-to-end solutions
Stay current on agent frameworks, LLM capabilities and limitations, and apply emerging patterns pragmatically in production.

we are a rapidly growing, award-winning B2B tech unicorn with an ambitious mission to revolutionize the payroll & payments industry. With over $400M raised from multiple tier-one investors, our innovative technology provides a comprehensive solution for managing global workforces, encompassing everything from hiring and onboarding to managing and paying employees in over 160 countries.
We're building AI to tackle one of the hardest operational challenges in business: getting workforce management right across many markets, each with its own tax rules, labor laws, payment rails, and constantly shifting policies.
In this role on our AI team, you'll build and scale core AI systems that power our company's products. You'll work across agentic document intelligence, autonomous agents, compliance AI, and ML-powered insights, prototyping quickly, building robust evaluations, and shipping production-grade AI with real business impact.
What You'll Do
Build and ship AI/ML solutions using LLMs, agents, RAG, and document understanding models, alongside classic ML
Prototype quickly, validate feasibility, and turn strong POCs into production systems
Evaluate models and architectures, apply testing and guardrails to improve agent and service reliability
Research and apply emerging techniques: multimodal/document AI, agentic frameworks, synthetic data generation, and new architectural approaches
Work cross-functionally with product, R&D, and compliance teams to deliver end-to-end solutions
Contribute to scalable, secure architecture and engineering best practices for AI delivery.

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.