לוח משרות דרושים, לחיפוש זה נמצאו 34,125 משרות

We are building the core infrastructure and operating system for autonomous platforms.
Our platform enables real-world autonomous operations by coordinating drones, docking stations, edge systems, operators, and external systems in real time. It serves as the operational backbone for mission execution, command and control, communication, telemetry, orchestration, and fleet coordination across distributed autonomous environments.
This is not a typical backend role.
You will work at the intersection of distributed systems, Command & Control (C2), autonomous platforms, and real-world operations.
We are looking for engineers who are comfortable entering complex and unfamiliar systems, quickly understanding how they work, and turning operational concepts into clean software models and architectures.
You should enjoy reasoning about entities, relationships, state, events, commands, missions, resources, and system boundaries - and designing software that represents and coordinates them reliably.
The platform includes:
Command & Control capabilities for autonomous systems
Fleet orchestration and mission lifecycle management
Real-time communication between distributed autonomous components
Modeling and management of operational entities, states, and relationships
Telemetry, video, and operational data processing
Device interoperability across heterogeneous autonomous platforms
SDKs and APIs for platform integration and extensibility
AI agents and intelligent automation interacting with operational systems
As a Senior Engineer, you will help shape the technical foundation and domain architecture of the platform while solving complex problems across distributed systems, autonomous operations, and Command & Control.
Responsibilities
Design and build software systems for autonomous platform operations and Command & Control
Translate complex operational requirements and domain concepts into robust software architectures
Model operational entities, relationships, state transitions, commands, events, and workflows
Lead architecture and system design decisions across services, communication layers, and domain boundaries
Build scalable, low-latency, resilient production services
Design reliable communication and messaging flows between distributed system components
Solve complex challenges involving concurrency, scalability, latency, synchronization, and fault tolerance
Build abstraction layers for integrating heterogeneous autonomous devices, platforms, and external systems
Develop internal SDKs, APIs, and reusable infrastructure components
Design systems that can be consumed and operated by humans, autonomous components, and AI agents
Improve observability, debugging capabilities, and operational reliability
Drive engineering best practices, clean architecture, and code quality standards
Explore new technologies and approaches when existing solutions are not sufficient

we are a leading company specializing in the development and manufacturing of autonomous drone systems. Its solutions combine hardware, software, and AI-driven technologies to support complex, large-scale operations. our company works primarily with defense industries, such as military and law enforcement agencies worldwide. The company is part of Ondas Holdings and operates as a wholly owned subsidiary.
We are looking for a System QA & Test Engineer to join our QA team and work on advanced, multi-disciplinary systems.
This is a hands-on role combining system testing, software and hardware integration, troubleshooting, and field activities. We are looking for people who enjoy working with complex systems, investigating problems, and spending time both in the lab and in the field.
Responsibilities
Plan and execute system and functional tests.
Perform integration and end-to-end testing across software, hardware, and system components.
Take part in field tests, trials, and system integration activities.
Work with simulators and different system interfaces.
Perform basic API testing using tools such as Postman.
Work in Linux environments for testing and troubleshooting.
Analyze logs, test results, and system behavior to identify issues.
Troubleshoot problems during lab and field testing.
Report and track defects using Jira.
Work closely with Software, System, Hardware, Integration, and Operations teams.

we are a leading company specializing in the development and manufacturing of autonomous drone systems. Its solutions combine hardware, software, and AI-driven technologies to support complex, large-scale operations. our company works primarily with defense industries, such as military and law enforcement agencies worldwide. The company is part of Ondas Holdings and operates as a wholly owned subsidiary.
We are looking for an system validation manager to lead the companys end-to-end experimental and system-validation activities for complex unmanned and multidisciplinary systems. The role requires a strong engineering leader who can translate system requirements into well-defined experiments, lead hands-on execution, analyze results, and deliver clear engineering conclusions and recommendations.
Responsibilities
Own test and validation activities from requirement understanding through planning, execution, analysis, reporting, and closure.
Work with System Engineering and R&D teams to define test objectives, scenarios, methods, instrumentation, and measurable pass/fail criteria.
Write professional test plans, procedures, readiness checklists, and final test reports with clear traceability to requirements.
Plan and lead laboratory, ground, field, and flight-test campaigns for UAVs, docking stations, payloads, communication systems, and other subsystems.
Ensure full test readiness: system configuration, test equipment, instrumentation, safety, range/logistics, personnel, and data collection.
Lead hands-on test execution, troubleshoot issues in real time, and make sound engineering decisions in dynamic field environments.
Analyze logs, telemetry, measurements, and system behavior; identify failures, trends, root causes, and gaps requiring further investigation.
Drive post-test debriefs, conclusions, lessons learned, corrective actions, and regression/verification activities.
Lead and develop the experiments/test team, establish professional standards, prioritize activities, and continuously improve test methodology.

we are a leading company specializing in the development and manufacturing of autonomous drone systems. Its solutions combine hardware, software, and AI-driven technologies to support complex, large-scale operations. our company works primarily with defense industries, such as military and law enforcement agencies worldwide. The company is part of Ondas Holdings and operates as a wholly owned subsidiary.
We are looking for an experienced Technical Writer.
Responsibilities
Write, update, and maintain technical and operational documentation, including manuals, procedures, work instructions, user guides, and maintenance documentation.
Write, edit, format, and organize documents in Microsoft Word according to defined templates, style guides, and documentation standards.
Update and modify diagrams, process flows, and flowcharts using Microsoft Visio.
Collect, analyze, and structure technical information from engineering teams, product teams, and subject matter experts.
Collaborate with technical teams and SMEs to create, review, validate, and update documentation.
Ensure documentation is accurate, clear, consistent, and suitable for the intended audience.

our company secures AI apps, agents and models from build to production. Enterprises are shipping AI faster than they can secure it, attack techniques evolve constantly, and every company draws the line on acceptable behavior in a different place. No enterprise can keep up with that on its own, and our company is how they do it. At the heart of the platform sit two engines: the attack engine finds the failures before attackers do, and the defense engine blocks them in production without breaking legitimate traffic.
We are hiring a Senior PM who will lead the strategy, vision, and execution of our core engines. This PM sits inside the DS workflow - and can read the notebooks, argue about the eval set, query the data themselves - and translate between model reality and customer reality in both directions.
This is not a PM role that writes a PRD and waits. It is a PM role where I pulled the data, heres what moved the F1, and heres what it means for customers is the expected level of answer.
What you'll own
Attack engine roadmap. Coverage and depth of adversarial techniques, novel-attack generation quality, agentic/multi-turn testing, zero-day and model-risk research productization. You decide which harm categories, modalities, and attack families we invest in next, and you defend that call with evidence.
Defense engine roadmap. Classifier quality across policies and languages, the precision/recall/latency triangle, customer-specific tuning, and the path from "a red team finding" to "a guardrail that blocks it." Closing that attack→defense loop is the single highest-leverage thing this role can do.
Evaluation as a product surface. Own how we define, measure, and publish model quality - benchmark datasets, eval harnesses, golden sets, regression gates, and the metrics customers actually see. When a customer asks "how accurate is this," the answer and the evidence behind it are yours.
Data strategy for the engines. What data we collect, label, retain, and feed back into training; how customer feedback (overrides, false-positive reports, session feedback) becomes a training signal; and how all of it stays within our customer data commitments
The DS↔Engineering seam. Model artifacts, schemas, and pipelines. You keep that contract explicit and get research into production.
Day to day
Run discovery with AI Product Owners, security engineers, and CISOs on what attacks they fear and what false positives they can't tolerate
Turn fuzzy customer harm into a testable engine requirement with a measurable bar
Prioritize the DS backlog with the DS lead; make the hard calls on coverage vs. depth vs. latency
Own the quality narrative for sales, solution engineering, and security reviews - backed by numbers you can defend
Partner with design and platform PMs so engine capability becomes a usable product, not a config flag
Track competitive and academic movement in adversarial ML and guardrails, and translate it into roadmap pressure.

We are looking for a Head of RL Gym to own the end-to-end delivery, quality, and scaling of RL Gym programs for our strategic AI partners.
You will work closely with frontier-model teams, Delivery, Product, Sales, Research, and Operations to turn complex customer and model-development needs into high-quality, scalable programs.
What Youll Own
Own partner delivery from scoping through launch, execution, scaling, and iteration.
Translate research and model needs into clear program requirements, workflows, and success metrics.
Work closely with frontier-model teams to identify model gaps and design RL Gym programs that address them.
Own quality standards and quality assurance, ensuring consistent, reliable outputs across programs.
Build the processes, systems, and operating model required to scale programs efficiently without compromising quality.
Partner with Sales on customer discovery, solution design, scoping, and expansion opportunities.
Work closely with Product to translate partner needs and operational learnings into product requirements and roadmap priorities.
Build strong feedback loops across customers, model teams, Sales, Product, and Operations.
Own operational performance across quality, capacity, timelines, cost, throughput, and partner satisfaction.
Build and lead the program management and operations function as the business scales.

we are looking for a Lead Endpoint Researcher.
What You'll Do
Research AI coding agent runtimes, reverse-engineer how tools such as Claude Code, Cursor, Copilot CLI, Codex, and emerging agents execute commands, access files, spawn processes, and interact with the operating system.
Design agent sandboxing architectures that safely constrain filesystem access, networking, process execution, credentials, and other sensitive endpoint capabilities.
Explore and implement OS-native isolation mechanisms across macOS, Windows, and Linux, including containers, namespaces, sandbox profiles, virtualization, restricted processes, and permission models.
Build endpoint "harnesses" that allow TokenShift to intercept, control, observe, and modify agent execution flows.
Design infrastructure for deploying and running local LLMs across heterogeneous endpoint fleets, accounting for operating system, CPU architecture, GPU availability, memory constraints, and hardware acceleration.
Evaluate local inference runtimes and model formats such as llama.cpp, MLX, ONNX Runtime, Ollama, vLLM-compatible runtimes, and emerging alternatives.
Develop mechanisms for model discovery, installation, versioning, updates, health monitoring, and lifecycle management across large numbers of developer machines.
Research hardware-aware model selection and routing, deciding which models can run effectively on Apple Silicon, NVIDIA GPUs, Windows workstations, Linux machines, CPU-only environments, and other endpoint configurations.
Build benchmarks that measure model latency, throughput, memory consumption, startup time, resource contention, and developer experience on real endpoint hardware.
Investigate security boundaries between local models, cloud-hosted models, agents, MCP servers, shells, and enterprise resources.
Collaborate with the core CLI and platform teams to take endpoint research from prototype through production.
Track the rapidly evolving AI agent, sandboxing, and local inference ecosystems and proactively identify technologies should support.

As an Adversarial AI Evaluation Scientist, you will review and validate adversarial agent tasks: indirect prompt-injection (IPI), data-exfiltration, tool-misuse, and related attack scenarios to ensure each one is realistic, fair, deterministic, reproducible, and correctly graded. You will decide whether a scenario genuinely demonstrates a model vulnerability or merely an artifact of how the task was built, and you will combine adversarial-security judgment with statistical analysis and LLM-assisted tooling to find quality issues, analyze attack-success patterns, and recommend or automate improvements.
Responsibilities
Review adversarial tasks for realism and threat-model soundness: is the attackers capability plausible, can the attacker actually reach the poisoned content, and is the target system a realistic one?
Assess attack fairness and attribution: confirm the harm is driven by the adversary (not pre-baked into the system prompt, vouched for by a trusted layer, or otherwise rigged), so a careful agent could plausibly pass.
Validate grading correctness: verify that success/failure criteria, reward logic, and deterministic checks detect the intended harm, with no reward hacks, token-collapse, or evadable gates.
Measure and interpret attack success rate (ASR@K) and its variance; verify deterministic, reproducible behavior across repeated executions.
Judge attack quality - novelty vs. known patterns, sophistication, stealth, and coverage across harm types and target surfaces.
Identify anomalies and systemic issues across large sets of tasks (e.g. scenarios that succeed ~100% of the time against every model - a red flag for an unfair task, not a win).
Define validation guidelines, acceptance criteria, quality thresholds, and release gates for adversarial tasks.
Use LLMs and AI models to analyze attack failures, identify root causes, and assist with task or grader fixes.
Build automated validation, regression, and quality-monitoring pipelines for adversarial task sets.
Work closely with red-teamers, researchers, and task authors to improve attack design, fairness, and reliability.