לוח משרות דרושים שרונה, לחיפוש זה נמצאו 7,202 משרות

We are looking for a hands-on Senior RAN Systems Engineer to lead technical evaluations and Proof-of-Concept activities across our RAN R&D organization.
You will take complex engineering questions and turn them into structured, reproducible evaluations that support clear technical decisions. You will define the scope and success criteria, analyze the relevant evidence, run the required lab, simulation, automation, or data-analysis workflows, and present clear conclusions and recommendations.
Your work will directly influence engineering priorities and help teams reach faster, better-supported technical decisions.
This is a senior individual-contributor role combining strong RAN knowledge, hands-on investigation, structured problem-solving, and end-to-end technical ownership.
What you will do:
Own selected RAN POCs and technical evaluations from initial definition through a clear, evidence-based conclusion.
Define the scope, evaluation approach, success criteria, required evidence, and execution plan.
Analyze logs, traces, configurations, software builds, test results, ticket history, and relevant lab or field data.
Run evaluations using lab environments, simulations, automation frameworks, dashboards, and other engineering tools.
Develop practical Python or shell scripts for data preparation, validation, and repeatable analysis.
Reproduce key findings, identify missing evidence, and clearly communicate assumptions, limitations, and recommendations.
Work with Development, QA, System Integration, Lab, Release, Field, and Radio teams to close technical and data gaps.
Convert recurring manual activities into reusable scripts, templates, checklists, and working practices.

You'll work end-to-end: agent design, tool implementation, retrieval quality, integration into existing product UIs, cloud infrastructure, and evaluation/observability. The platform already ships to customers - you'll extend it, raise its quality bar, and help define where it goes next.

What you'll do

Design and evolve agents - build LLM agents with tool use, routing, and human-in-the-loop flows.
Implement tools and integrations - expose product capabilities to the agent, with multi-tenant context, via internal APIs and MCP servers.
Own retrieval quality - contribute to our RAG pipeline end-to-end: ingestion, embeddings, vector search, and reranking.
Define and evolve host integration contracts - collaborate with host application teams to integrate the assistant into product UIs built on different frontend stacks. You own the shared remote module and the integration API; host teams own their stacks.
Drive evaluation-led development - write evaluators (rule-based, LLM-as-judge, multi-turn), maintain CI eval gates, and use traces and feedback to debug production behavior.
Operate the platform - own deployments, observability, and the performance and cost of LLM-backed services.
Establish engineering practices** for AI-specific work: prompt versioning, eval coverage, testing, and code review.

*Part-Time or Full-Time Opportunity

Responsibilities:
The research focuses on designing next-generation rack and SuperPod architectures that combine elasticity through resource pooling with optimal performance enabled by holistic co-design across applications, parallel programming models, interconnect fabrics, and compute, memory, and switch chip architectures. On the communication side, the work architects scalable, high-bandwidth, and low-latency scale-up fabrics across multiple chips at rack and SuperPod scale. On the memory side, it explores richer protocol semantics beyond traditional load/store operations to reduce unnecessary data movement and rethinks memory hierarchies to expose large-scale capacity with near-local latency. On the compute side, it analyzes the requirements of General Compute and Generative AI workloads and their parallel programming models to fully leverage large-scale system resources.
Investigate and prototype new architectural features, including but not limited to:
o Tiered Memory: Explore advanced memory organizations, including hardware-managed caching, to enable software-transparent, fine-grained promotion and demotion of cache lines across the memory hierarchy.
o Prefetching/Speculation: Evaluate and design existing and next-generation hardware prefetching and speculation mechanisms to effectively hide local and remote memory latencies at rack and SuperPod scale.
o Near-Memory/Network Processing: Develop support for key primitives executed at the memory and network layers to minimize unnecessary data movement across sparse, dense, and pointer-based data structures.
o Workload-Centric Co-Design: Study optimal parallelization strategies at rack and SuperPod scale for both General Compute and Generative AI workloads, and design dedicated hardware support for widely used parallel programming primitives such as RPCs and collective communication.
Write reports and papers on the research results and present them.

We're seeking a Sr. Director, Platform to lead our software platform group - a team of ~45 engineers distributed across Israel, India, Ukraine, and the UK. This is a hands-on leadership role combining people management with technical direction, driving a cloud-native Kubernetes-based platform that powers RAN deployments globally.
You'll report to the VP of RANOps R&D and play a critical role in shaping how we build and scale our platform software.
Key Responsibilities:
Lead and develop a multi-regional engineering organization (~45 engineers) across 5 teams and 4 countries and multiple time zones
Drive technical strategy for the cell-site software platform, built on Kubernetes (EKSA/SUSE) in cloud-native architecture
Champion cross-team collaboration within the broader R&D organization
Spearhead adoption of AI-assisted development tools and workflows to accelerate engineering productivity
Improve system debuggability and observability across complex, distributed deployments
Lead modernization efforts for legacy codebases while maintaining production stability
Define and formalize team-level KPIs aligned with SAFe practices
Enhance CI/CD pipelines and automation to improve delivery velocity and quality
Balance real-time constraints, resource management, and networking requirements inherent to RAN software

Eitan Medical is an international Medical Device company that develops and manufactures innovative medical equipment. Specifically, we develop infusion pumps and their related accessory products. Job Description: Eitan medical is looking for a highly motivated Software QA Engineer with strong manual testing capabilities, hands-on automation experience, and practical usage of AI tools for software quality and testing activities. As part of the Digital Health Department, you will be responsible for designing, planning, and executing test scenarios for complex medical software systems. The role combines manual testing, automation development, backend/API validation, and AI-assisted QA workflows to ensure high-quality and reliable products used in real-life patient care. You will work closely with R&D, Product, and System Engineering teams in a dynamic and fast-growing environment. Direct Manager: Digital Health SW QA Team Leader