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We are looking for highly motivated, unique individuals to join our company for the next phase of our fast moving story.
This Software Engineer role is an opportunity to be one of the first employees to join the team and make a significant impact. Through impeccable software, you will help our customers solve one of the biggest pain points they have encountered, making their life better.
What Youll Do?
Take a significant role in our core backend team.
Design & develop new micro services.
Solve complex architecture problems.
Develop infrastructure components to be used across multiple micro services.
Build isomorphic JavaScript packages to be shared across a client & server mono repo.
You will use Google Cloud Platform to bundle, build and deploy our products.
What We Offer?
A competitive compensation package of early-stage equity.
Take a major role in the companys success.
Join our experienced highly motivated and fast-moving team.
To do a lot of fun while developing features to improve peoples life.
Our Tech Stack
Node 16, MongoDB Cluster, Cloud Functions, Mono Repo (Lerna), isomorphic JS components, Micro services, Everything is in GCP.

This is a unique opportunity to build modern DevOps infrastructure from the ground up, drive engineering automation, introduce AI-powered development practices, and shape the way complex multidisciplinary products are developed, tested, and delivered. Responsibilities
Define and execute the DevOps strategy, architecture, and roadmap across Software and Embedded Software R&D.
Build and maintain scalable CI/CD pipelines, automation frameworks, and engineering infrastructure.
Lead the implementation of automated testing, release management, artifact traceability, and quality monitoring solutions.
Drive the adoption of AI-powered tools, LLMs, and intelligent automation within development and DevOps workflows.
Own and improve engineering toolchains, including Git, Jira, and related integrations.
Partner with Software, Embedded, QA, and IT teams to enhance engineering efficiency and product quality. Build, mentor, and lead a high-performing DevOps team while remaining highly hands-on.

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 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.

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.

We are seeking a Senior AI Researcher to lead post-training evaluation, red-teaming, and reinforcement learning (RL) gym audits on open-weight models. The ideal candidate will establish rigorous benchmarking methodologies, evaluate large language models (LLMs) against complex threats like Indirect Prompt Injections (IPI), and construct post-training evaluation pipelines that accurately measure realistic frontier-level security capabilities.
Key Responsibilities
RL Post-Training & Benchmarking
Execute post-training runs (e.g. GRPO) using mainstream open-weight generalist models against security-focused RL environments, targeting threat vectors like Indirect Prompt Injection (IPI).
Reward Diagnostics & Trace Analysis - Analyze live loss curves and rollout traces to identify reward hacking, lazy policy convergence, and flawed or over/under-specified verifiers.
Task & Environment Auditing
Review tasks and multi-turn environments (including tool use, web navigation, and computer use) for realism, threat model accuracy, data distribution, and dataset balance.
Performance Reporting (Gym Cards)
Generate comprehensive evaluation cards detailing hill-climbing performance uplift across checkpoints, failure modes, tokens/turns per rollout, and task-level success rates.
Integration & Orchestration
Integrate dockerized environments (e.g., Harbor format) into internal training frameworks, optimizing reset/statefulness semantics, concurrency, and throughput ceilings.

we are seeking an experienced Malware Research Team Leader to lead a team of malware researchers, combining hands-on research with people leadership. This role presents an opportunity to lead an established team, strengthening its skills through ongoing training and guidance while ensuring high-quality deliverables.
The position balances managerial responsibilities with hands-on technical work (approximately 50% each). The ideal candidate demonstrates a positive, proactive attitude and excels as a reliable team player.
Key Responsibilities:
Mentor and manage a team of malware researchers, including recruitment
Conduct short, focused malware research under tight timelines
Detect and document emerging malware trends
Evaluate and enhance team accuracy and performance
Regularly share knowledge with team members and collaborate across teams and departments
Continuously improve malware analysis and review processes
Develop automation processes for malware identification at scale.

We're looking for an exceptional, hands-on technical leader to join our core engineering team as a true force multiplier and technological anchor for the company. You'll have the flexibility to drive high-impact initiatives from conception to production, own complex technical and AI domains end-to-end, or guide and level up a focused delivery team, operating with a high degree of autonomy in an ambiguous, fast-moving environment.
What You'll Do:
Drive strategic initiatives: lead complex technical projects, AI feature developments, and core architectural components with an ownership mindset.
Architect for AI & data at scale: design robust, scalable infrastructure and pipelines capable of handling massive volumes of cloud infrastructure data.
Bridge product, AI & tech: collaborate closely with Product Managers and data specialists to translate business roadmaps into high-performance technical systems.
Promote engineering excellence: establish development guidelines, lead design and architecture reviews, and raise the bar for code quality across teams.
Mentor & guide: act as a technical mentor to fellow engineers through hands-on collaboration, deep code reviews, and architectural consultation.

our company's RL Gyms team builds adversarial environments and evaluations that the world's leading AI labs use to test and train their most capable agents. Every engagement is a delivery project: a lab specifies what it needs to measure, and we produce the environments, adversarial tasks, and verifiers that measure it, against a deadline and a quality bar the customer sets.
Our customers are the frontier labs. You'll work alongside the people building the models everyone else is reading about, on the problems they haven't solved yet, and what you ship becomes part of how they decide whether an agent is safe to release. Very few roles put you that close to the frontier.
We're looking for a Technical Project Manager to own those projects. You'll run several lab engagements at once, each with its own researchers, engineers, and reviewers. You decide who works on what, you hold the schedule, and you decide whether a batch is good enough to ship.
The field is cutting edge and the projects change shape faster than any playbook can describe.
What you'll do
Project ownership
Own concurrent lab engagements end-to-end: scope, milestones, delivery, and status
Turn a customer specification into a task plan with your researchers and engineers: how many tasks, in which environments, against which models, by when
Track every batch through the pipeline (in progress, under review, approved, delivered) and keep the delivery record accurate enough that anyone can read the state of a project in a minute
Surface a slipping deadline early, with the tradeoff already worked out: cut scope, add capacity, or move the date
Researcher allocation and quality
Allocate researchers, task writers, and reviewers across projects based on throughput and specialty
Own their onboarding: brief them on the environment, the attack pattern, and the acceptance criteria, then get them to their first accepted task fast
Give quality feedback on rejected work and spot the patterns behind repeat rejections
Manage the review chain so that no batch reaches the customer before it has cleared internal review.

We are hiring a hands-on AI expert to shape our intelligent platform, organizing complex real estate data into actionable insights and predictive models that power smarter investment decisions.

Responsibilities
AI/ML Architecture: Design and implement scalable AI systems using models such as LLMs (e.g., OpenAI, Anthropic) and computer vision (e.g., YOLO, CLIP).
MCP Integration: Architect and orchestrate model context flows using MCP or similar frameworks to deliver personalized, context-aware experiences
FinTech Platform Integration: Embed intelligent features into our backend and frontend systems, ensuring seamless UX and robust performance
Hands-On Development: Write production-grade code across Python, TypeScript, and Node.js. Own key modules and guide technical decisions
Team Leadership: Mentor a few developers, set engineering standards, and foster a culture of experimentation and excellence
Data Strategy: Collaborate with product and data teams to define training datasets, feedback loops, and model evaluation pipelines.