דרושים » תוכנה » Staff Technical Program Manager

We are seeking an AI Networking Architect to join the Networking Research Group. This role bridges the gap between emerging AI workloads and the data center infrastructure that powers them, working at the intersection of AI applications, distributed systems, networking hardware, and software architecture. You will join a focused team of multidisciplinary engineers driving AI workload optimization through deep application understanding, network analysis, and end-to-end systems thinking. Your insights will directly shape our products across the full stack - from applications and software libraries to hardware architecture and physical design.


What you'll be doing:
Model the performance of complex AI workloads to identify bottlenecks and recommend system-level optimizations.
Analyze new AI models, distributed training techniques, and inference workloads to understand their infrastructure requirements.
Build simulation and hardware platforms, run real AI workloads on them, and develop analytical tools to evaluate trade-offs across compute, memory, storage, and network behavior.
Translate research insights and workload behavior into actionable software, xhardware, and networking architecture requirements.
Partner with architecture, software, and product teams to influence our future networking and AI infrastructure roadmaps.
Drive architectural innovation by applying deep workload analysis to production machine learning frameworks.

We're looking for our **first Forward Deployed Engineer**. You'll sit directly with our customers' engineering teams and get their workloads onto Impala - from the first scoping conversation to a production endpoint carrying real traffic, at a cost and latency profile they couldn't hit anywhere else.
This is an engineering role. You will write code every day, in customer repos and in ours. It also carries pieces of solutions architecture, product management, and pre-sales, and you should want that mix rather than tolerate it. Ambiguous business goals come in; observable, benchmarked, production services go out.
As the founding FDE you also define the function: what a POC looks like, what we promise and measure, which patterns get productized, and how the field feeds the roadmap. The next FDEs will work from what you build here.
What You'll Do
- **Own customer outcomes end to end:** problem framing, evaluation design, migration, deployment, benchmarking, monitoring. You're the technical owner from first call through expansion.
- **Win the POC:** turn a vague objective into a tight spec and a working proof of concept fast, with explicit quality, latency, throughput, and cost-per-token targets - and hit them.
- **Tune serverless inference for real workloads:** model and engine selection, batching strategy, KV cache behavior, speculative decoding, quantization, parallelism, cold-start and autoscaling behavior under bursty traffic. Diagnose regressions down to the inference engine.
- **Migrate workloads onto Impala:** move customers off OpenAI-compatible APIs, self-managed vLLM, SageMaker, or their own GPU fleets - and prove out the quality and cost delta with numbers.
- **Guide model strategy:** advise on open-weight model selection, distillation, and fine-tuning for specific tasks; help customers get from a general-purpose frontier model to a smaller, faster, cheaper one that holds quality.
- **Close the product loop:** bring the field back into the roadmap - write the PRDs, land the PRs, and turn one-off customer work into platform features.
- **Be the technical anchor in the room:** support sales on complex evaluations, run technical onboarding, earn trust with staff engineers and CTOs.

We are seeking a senior, hands-on End-to-End Architect to define and drive system architecture for next-generation AI and accelerated computing platforms. Join our E2E Architecture group, where we build large-scale solutions that bring together high-capacity communication systems, software, physical devices, and infrastructure.

In this role, you will own system-level architecture across multiple technologies and organizational boundaries. You will interpret product and business requirements to develop complete architectural solutions, identify gaps across system components, and drive technical alignment from early definition through productization and deployment.



What Youll Be Doing:

Define end-to-end system architectures for Spectrum-X solutions, covering networking, software, hardware, infrastructure, and application integration.

Lead architecture tasks for the entire solution lifecycle, from requirements and system definition through implementation, validation, deployment, and optimization.

Collaborate with systems, software, model, networking, platform, and application teams to deliver integrated, high-performance solutions.

Evaluate and integrate new software and hardware technologies relevant to core Spectrum-X capabilities, including load balancing, telemetry, congestion control and more.

Translate product and business requirements into system architecture, component requirements, interfaces, and technical execution plans.

Identify architectural gaps, dependencies, tradeoffs, and performance bottlenecks across the end-to-end system.

Work closely with productization teams, including test, validation, deployment, and support, to ensure the complete solution is reliable, scalable, and ready for production.

Author architecture documents, design specifications, technical requirements, and technical publications.

Provide technical leadership and guidance to engineering teams throughout implementation and integration.

We are at the forefront of the AI revolution, delivering brand new accelerated compute platforms for global impact. Our Network Architecture group is seeking a talented and motivated Sr. Software Engineer to build the agentic workflows that our architects use in their daily work. The software at the center of this role is our hardware network simulation environment - you will design multi-step agent workflows over it, engineer the context that grounds them in our own specifications and source code, and optimize their runtime performance. If you are passionate about building the practical infrastructure that brings intelligent agents to life, we want to hear from you.


What you'll be doing:
Build agentic workflows - loops, graphs, and multi-step pipelines - that carry real hardware network simulation and analysis work end to end.
Engineer the context these workflows run on, turning our simulation models, specifications, design documents, and source code into context that makes agents accurate in our domain.
Work closely with network architects to understand their workflows and translate them into agent workflows they use daily.
Optimize the runtime performance of our simulation tooling on these platforms, including execution time, compute cost, and end-to-end latency.
Define evaluation and regression testing for agent workflows, so that changes to a prompt, a graph, or a context source are measurable.
Build observability across agent runs: what the agent did, where it failed, and why.
Champion guidelines for secure and reliable agent workflows, including data handling, access control, and interaction boundaries.
Serve as a key technical resource for solving sophisticated integration issues between agents and internal tooling.

Were looking for a Senior MLOps Engineer to be a core driver in how our product empowers security teams. You will be expected to deeply understand customer needs and translate them directly into product features that deliver real value. You'll own key parts of our frontend stack, drive key architectural decisions, and turn complex security data into clear, actionable business insights.

As we scale our AIDR product and expand deeper into model-driven security intelligence, we are looking for a Senior MLOps Engineer to own the infrastructure, tooling, and operational foundations that power our NLP and LLM training, evaluation, and deployment workflows.

You will architect and operate the systems that enable us to train, fine-tune, deploy, and monitor models at scale making our ML reliable, fast, cost-efficient, and production-ready.

This is a high-visibility, high-impact role where you will partner closely with DevOps, Backend, Data, and Product to establish world-class ML infrastructure from the ground up.

What Youll Do
Build & Scale ML Pipelines
Design, build, and maintain pipelines for training, fine-tuning, evaluating, and deploying NLP and LLM models across GPU and CPU environments.
Establish LLM-Focused CI/CD
Implement automated CI/CD workflows for ML models, including benchmarking, testing, performance gating, and production deployment.
Optimize Runtime & Inference
Select and optimize serving frameworks for low-latency, high-throughput inference, ensuring reliability and scalability.
Own ML Infrastructure
Manage training environments, experiment tracking, model registries, artifact versioning, and distributed training systems.
Operational Excellence
Monitor and optimize production models for performance, cost efficiency, availability, and observability.

Join our company, an innovative startup building a fully-managed LLM-inference platform, that enables data heavy enterprises to perform any AI task at any scale without limits.
We're looking for an Experienced Performance Researcher to join our founding team. Youll be responsible for building and optimizing scalable cloud infrastructure solutions tailored for AI workloads. This role offers a unique opportunity to directly shape our infrastructure strategy, improve system reliability and performance, and contribute to establishing our company as a leader in adaptive AI compute management.
Join us to tackle the magic that make AI tick under the hood and build the backbone powering the AI revolution.
What Youll Do
- Design and build high-performance distributed inference pipelines for LLMs, focused on large-batch, non-real-time scenarios.
- Optimize GPU memory usage, kernel execution, and communication across nodes (NCCL, MPI, etc.).
- Own CUDA kernels, compiler-level tricks, and multi-GPU scheduling logic.
- Lead profiling and performance tuning for throughput, and cost- down to the kernel level.
- Collaborate with infra, product, and research teams to define SLAs, resource allocation logic, and runtime behaviors.
- Help build the core infrastructure that will run LLM workloads across hybrid GPU environments (cloud/on-prem/self-hosted).

Forward Deployed Engineering sits at the frontier of that work. We embed with our most strategic enterprise customers, running on-prem, air-gapped, and heavily regulated environments, and own the technical arc from discovery through production.

Your day might include designing a solution architecture for a high-impact agentic use case, debugging a production routing issue deep in our codebase, deploying a new MCP server with a customer team, or aligning with a CTO on a rollout plan; all in service of outstanding outcomes and rapid time to value for the organizations we work with.

This is a founding function. The first FDEs will define how we conducts field engineering: the standards, playbooks, and technical bar. The work is hands-on, commercially scoped, and high-leverage: youll partner closely with Product, Research, Platform, Security, and GTM, and what you learn in production directly shapes what the core team ships next. If you thrive under ambiguity, make fast and sound decisions when the stakes are high, and get energy from closing the gap between what customers hit in production and what we build next, this role is for you.

In This Role You Will
Own technical delivery across multiple deployments from first prototype to stable production, including core engineering work that unblocks strategic accounts.
Lead deep technical diagnosis and remediation in our codebase and production environments, model routing and gateway behavior, large-PR scaling, multi-pass behavior, performance and reliability issues.
Build customer-specific extensions (MCP servers, agent skills, platform integrations) that deliver immediate value and become durable product capability; upstream them into the main roadmap where appropriate.
Embed closely with customer engineering teams: serve as their senior technical counterpart for hands-on debugging, architecture reviews, and joint implementation.
Scope work, sequence delivery, and remove blockers early; make deliberate trade-offs between scope, speed, and quality to protect delivery timelines.
Partner with Product, Research, and Platform to convert account learnings and eval-driven feedback into roadmap items, specs, and prioritized engineering work.
Codify working patterns into tools, playbooks, runbooks, and reproducible benchmarks that Support and account teams can operate independently and help scale the field engineering function across the organization.

We are looking for a Sr. Staff Backend Engineer to join our team. This is a Hybrid (Ramat Gan) role, reporting to the Manager, Software Engineering in the Exposure Management & Security Operations department. Avalor, now part of Zscaler, powers the data foundation behind Security Operations platform, ingesting, unifying, and contextualizing data from hundreds of security and business tools to power security modules that deliver actionable insights and measurable risk reduction to our customers. In this role, you'll own complex backend systems that sit at the heart of our data platform, designing and building services that ingest, process, and move massive amounts of data reliably and at scale for enterprise customers worldwide.

What youll do (Role Expectations)

Developing and maintaining high-quality, scalable backend services within a microservices architecture using Java/Spring Boot and Python
Designing and building data pipelines and ETL workflows that ingest, transform, and unify data from diverse sources at enterprise scale
Leading solutions from scratch to production; creating technical designs, driving architectural decisions and taking full ownership of complex projects - from design and implementation through deployment, monitoring performance, reliability and cost, and proactively driving improvements
Collaborating closely with cross-functional partners - product managers, frontend/backend engineers and UX designers
Serving as a technical anchor for the team - leading design discussions, setting engineering standards, and driving best practices across the codebase
Who You Are (Success Profile)

You thrive in ambiguity. You're comfortable building the path as you walk it. You thrive in a dynamic environment, seeing ambiguity not as a hindrance, but as the raw material to build something meaningful.
You act like an owner. Your passion for the mission fuels your bias for action. You operate with integrity because you genuinely care about the outcome. True ownership involves leveraging dynamic range: the ability to navigate seamlessly between high-level strategy and hands-on execution.
You are a problem-solver. You love running towards the challenges because you are laser-focused on finding the solution, knowing that solving the hard problems delivers the biggest impact.
You are a high-trust collaborator. You are ambitious for the team, not just yourself. You embrace our challenge culture by giving and receiving ongoing feedback-knowing that candor delivered with clarity and respect is the truest form of teamwork and the fastest way to earn trust.
You are a learner. You have a true growth mindset and are obsessed with your own development, actively seeking feedback to become a better partner and a stronger teammate. You love what you do and you do it with purpose.

Join our companys AI research group, a cross-functional team of ML engineers, researchers and security experts building the next generation of AI-powered security capabilities. Our mission is to leverage large language models to understand code, configuration, and human language at scale, and to turn this understanding into security AI capabilities which will drive our company AI future security solutions.
We foster a hands-on, research-driven culture where youll work with large-scale data, modern ML infrastructure, and a global product footprint that impacts over 100,000 organizations worldwide.
Key Responsibilities
Your Impact & Responsibilities
As a Senior ML Research Engineer, you will be responsible for the end-to-end lifecycle of large language models: from data definition and curation, through training and evaluation, to providing robust models that can be consumed by product and platform teams.
Own training and fine-tuning of LLMs / seq2seq models: Design and execute training pipelines for transformer-based models (encoder-decoder, decoder-only, retrievalaugmented, etc.), and fine-tune open-source LLMs on our company-specific data (security content, logs, incidents, customer interactions).
Apply advanced LLM training techniques such as instruction tuning, preference / contrastive learning, LoRA / PEFT, continual pre-training, and domain adaptation where appropriate.
Work deeply with data: define data strategies with product, research and domain experts; build and maintain data pipelines for collecting, cleaning, de-duplicating and labeling large-scale text, code and semi-structured data; and design synthetic data generation and augmentation pipelines.
Build robust evaluation and experimentation frameworks: define offline metrics for LLM quality (task-specific accuracy, calibration, hallucination rate, safety, latency and cost); implement automated evaluation suites (benchmarks, regression tests, redteaming scenarios); and track model performance over time.
Scale training and inference: use distributed training frameworks (e.g. DeepSpeed, FSDP, tensor/pipeline parallelism) to efficiently train models on multi-GPU / multi-node clusters, and optimize inference performance and cost with techniques such as quantization, distillation and caching.
Collaborate closely with security researchers and data engineers to turn domain knowledge and threat intelligence into high-value training and evaluation data, and to expose your models through well-defined interfaces to downstream product and platform teams.

We are looking for a Software Architect to lead the technical evolution of our next-generation AI cybersecurity platform. You'll work across teams to solve the company's most challenging architectural problems while remaining deeply hands-on - building prototypes, validating new technologies, and helping engineering teams deliver production-ready solutions. The role requires both deep engineering expertise and the ability to shape the architecture of large-scale data, cloud, and AI systems that power our platform.

Responsibilities:
Partner with engineering teams to solve complex architectural and technical challenges, leading the design of scalable, secure, and maintainable systems.
Build and validate proofs of concept (PoCs) to evaluate new architectures, data platforms, AI capabilities, and emerging technologies.
Drive technical direction across engineering, product, and data science, turning new technologies into production-ready solutions.
Lead architecture and design reviews, ensuring scalability, reliability, security, and operational excellence.
Mentor engineers through hands-on collaboration, code reviews, and system design discussions while promoting AI-native engineering practices.