לוח משרות דרושים AI Engineer משרה מלאה, לחיפוש זה נמצאו 174 משרות

Were looking for a hands-on AI Solutions Manager who combines deep practical knowledge of generative AI tools with strong business understanding. This role sits at the intersection of technology and impact - identifying real organizational needs and translating them into scalable, AI-driven solutions.

This role is perfect for someone who lives and breathes AI technology, stays ahead of the rapidly evolving landscape, and knows how to turn cutting-edge capabilities into real-world solutions.

What Youll Do

Collaborate with cross-functional teams to identify opportunities where AI can solve complex business and internal problems
Evaluate and select the right AI tools and models (Claude, Gemini, OpenAI, etc.) based on use case, limitations, cost, performance, and scalability
Design and implement AI-powered solutions and internal automations using LLM APIs and workflow platforms (e.g., N8N, Workato)
Build and optimize prompts, RAG pipelines, and AI agents while understanding model limitations and trade-offs
Architect scalable AI workflows and integrations across systems (e.g., Salesforce, Jira, Confluence, , etc.)
Lead hands-on projects from ideation to rollout, iterating rapidly to deliver value
Stay up to date with emerging AI capabilities and continuously evaluate new tools and frameworks
Educate and guide the organization how to adopt and use wisely AI tools and models

We are looking for a Senior Data Science Researcher to join Honeycomb's AI team, the team behind underwriting decisions, quote-time risk scoring, and portfolio analytics.

In this role, you will own entire research directions: framing the problem, deciding what's worth measuring, designing the approach from first principles, and turning
the result into production signal. The work is production-oriented applied research on hard, real-world datasets where standard recipes don't apply and the right method
often has to be invented for the problem.

What You'll Do

- Own open-ended research directions end-to-end - from a vague business question to a deployed, validated signal.
- Work across the team's core research areas - risk modeling, environmental and catastrophe modeling, and behavioral modeling - translating complex real-world processes
into reliable predictive signal.
- Deeply investigate existing production models - understand what they actually learn, where they break, and where the headroom is; identify and ship optimizations
grounded in that understanding.
- Design approaches from first principles when off-the-shelf methods don't fit the data.
- Build rigorous evaluations and own the result in production, not just the notebook.
- Document research clearly so findings are reproducible, auditable, and compound over time.

We are seeking a AI Engineer to join our team and implement advanced AI solutions in our travel and expense management products. The ideal candidate will have strong backend engineering expertise and enthusiasm for working with cutting-edge AI technologies.
You'll be responsible for designing and implementing backend services that integrate AI capabilities including large language models (LLMs), retrieval-augmented generation (RAG), and conversational interfaces into our consumer-facing products. This is an opportunity to expand your expertise into AI while leveraging your strong engineering foundation.
What Youll Do:
Design and develop scalable, high-performance backend services and APIs.
Implement integrations with AI platforms and services.
Work with AI specialists to translate AI capabilities into production-ready features.
Ensure high reliability, performance, and security of AI-powered systems.
Collaborate with product and engineering teams to gather requirements and deliver solutions.

Were looking for a talented AI-native Software Engineer to join our innovative R&D team!
We help our customers acquire better users and spend less doing it - and our core team owns the engine behind it. We turn raw customer data into ad-network conversions, end-to-end: through large-scale ML and data pipelines, event-driven delivery services on AWS, and the UI that ties it together. There's no narrow lane here - you own problems wherever they live in the stack.
We're an AI-native team: we treat AI and agentic tooling as a first-class part of how we build, and it's how a small team credibly owns this much surface area. We're looking for a high-ownership generalist who works this way (or is hungry to), and who thrives in a correctness-critical domain where a bug means real customer ad-spend going the wrong way.
Responsibilities:
Turn raw customer data into ad-network conversions end to end, building across the whole stack: ML and data pipelines, delivery services, and the customer-facing UI.
Take features from idea to production largely on your own, designing, shipping, monitoring, and iterating, using AI and agentic tooling as a force multiplier, and owning your systems in production where mistakes translate directly into customer spend.
Collaborate closely with the team lead, product, and data scientists to take models and ideas from prototype to reliable, scaled-up production.

We are looking for a hands-on wireless systems engineer to lead the development of a multi-RAT digital twin for our Open RAN solution. The digital twin will execute production RAN software-beginning with scheduler and MAC behavior-in a closed loop with PHY, channel, UE, traffic, and network models. It will allow engineering teams to design, evaluate, and compare features for LTE, 5G NR, and 2G without requiring a dedicated physical radio setup for every development cycle.
This is a senior individual-contributor role at the intersection of wireless systems, simulation, production software, and AI/ML. You will evolve an existing LTE end-to-end simulator into a scalable engineering platform for feature development, regression testing, performance optimization, and evidence-based pre-validation. Initial use cases include MAC scheduler and link-adaptation improvements, power control, mobility and interference scenarios, and neural-network-assisted channel estimation.
The successful candidate will understand that a useful digital twin must be both fast and trustworthy. You will define multiple fidelity levels-from rapid surrogate models to full PHY processing-and establish repeatable methods for calibrating the twin against lab or field reference data. The goal is to reduce dependence on continuous lab access while maintaining clear, measurable confidence in the simulation results.
What you will do:
Own the technical architecture and roadmap for a modular, multi-RAT RAN digital twin
covering LTE, 5G NR.
Integrate production MAC and scheduler software into deterministic, per-TTI/slot closed-loop simulations
through stable and maintainable interfaces.
Model the interaction among scheduler decisions, PHY processing, propagation channels, UE behavior, traffic, interference, mobility, HARQ, link adaptation, and power control.
Extend the current LTE simulation capability and define reusable abstractions that support additional 5G NR and
2G stacks without duplicating the platform.
Design a fidelity ladder that combines high-fidelity PHY execution with faster calibrated models or lookup/surrogate backends, selecting the least expensive model that is valid for each engineering question.
Develop and evaluate AI/ML-based RAN capabilities, including neural channel estimation, learned link adaptation or scheduling policies, and ML-based PHY or channel surrogates.
Build representative datasets and experiment pipelines; establish conventional algorithmic baselines;

We are looking for a hands-on Senior Applied AI Evaluation Engineer to improve how our RAN R&D organization analyzes engineering tickets, supports Root-Cause Analysis, recommends ownership, and learns from resolved cases.
You will build rigorous evaluation datasets, establish meaningful baselines, compare internal and approved external tools, analyze failure modes, and prototype improvements across retrieval, classification, prompting, agent workflows, and model selection.
This role is focused on measurable, evidence-based improvement rather than AI demonstrations. You will assess whether AI-generated conclusions are accurate, grounded in evidence, appropriately calibrated, and useful to engineering teams.
As a secondary area of focus, you will analyze engineering workflows at case and team level to identify bottlenecks, handoffs, dependencies, and opportunities for process improvement.
What you will do:
Define high-value RAN ticket-intelligence use cases, acceptance criteria, evaluation metrics, and quality guardrails.
Build and maintain representative, versioned evaluation datasets using resolved tickets, Root-Cause Analysis, logs, test evidence, code changes, reviews, reassignment history, and outcomes.
Establish current-tool and non-AI baselines before evaluating new LLM, RAG, search, or agent-based approaches.
Evaluate approved internal, commercial, local, and open-source solutions using secure and reproducible data-handling processes.
Measure retrieval quality, groundedness, diagnosis accuracy, citation support, routing recommendations, calibration, abstention, latency, cost, and human effort.
Design held-out, time-based, edge, and adversarial test cases while preventing data leakage and future-outcome contamination.
Analyze failures and turn incorrect conclusions, misrouting, unsupported claims, and missed evidence into prioritized improvements.
Prototype improvements in search, metadata, context construction, prompting, reranking, classification, agent workflows, and model selection.
Develop reusable evaluation pipelines, tools, services, APIs, dashboards, or documented workflows.
Work closely with AI, RAN, QA, System Integration, Release, Field, data, and engineering teams to review results and support evidence-based decisions.

We are looking for an experienced AI Engineering Manager to lead our AI team within R&D and drive the development and integration of AI capabilities across our RAN product portfolio.
The team works on production-focused AI solutions, including agentic AI for network operations, AI-driven optimization and automation, technical assistants, and automated root cause analysis. In this role, you will combine technical leadership, people management, and execution ownership to take AI initiatives from concept through productization and measurable impact.
You will define the technical direction and roadmap, lead and grow a multidisciplinary team of AI/ML, software and domain engineers, and work closely with R&D, Product, Systems and global teams to bring scalable AI capabilities into our products.
What you will do:
Lead the AI team and own its technical direction, roadmap and execution.
Drive AI/ML initiatives from concept and prototyping through production deployment and ongoing optimization.
Define clear success metrics and ensure AI solutions deliver measurable product and business impact.
Lead architecture and technical decision-making around AI models, platforms, tools and data.
Build and grow a strong multidisciplinary team, including recruitment, mentoring, performance management and career development.
Establish strong engineering practices around evaluation, scalability, reliability, monitoring and model lifecycle management.
Work closely with Product Management, Systems Engineering and R&D teams across Israel, India and the US.
Partner with RAN and domain experts to identify opportunities where AI can improve network performance, automation and operational efficiency.
Support customer-facing discussions, trials and PoCs where relevant.

we are looking for a Senior Backend Engineer with a strong passion for AI and intelligent systems to join our growing R&D team.
In this role, youll be building the backend foundation that powers our AI-enhanced security analytics, autonomous detection, and real-time decisioning systems. Youll work closely with AI engineers, product, and security experts to bring agentic intelligence into our companys runtime platform - transforming how modern apps defend themselves.
Youll architect and implement services that scale to massive volumes of runtime data, integrate large language models (LLMs), and support RAG-based workflows for contextual understanding and response.
Youll Be Great For This Role If You Love To:
Design and build scalable, resilient backend services that power our companys AI-driven runtime security platform.
Integrate LLMs and agentic AI components into production environments to enable autonomous detection and remediation capabilities.
Design evaluation frameworks for measuring model performance and reliability in real-world conditions.
Develop Retrieval-Augmented Generation (RAG) pipelines tailored to application security data.
Own backend architecture decisions - from data modeling to performance optimization - across Node.js, Python, and cloud-native environments.
Contribute to our infrastructure, leveraging CI/CD, Kubernetes, Docker, and modern observability practices.
Mentor peers and help shape our companys AI engineering culture.

our company already runs agents in production. Our WAF Copilot takes a freshly disclosed CVE and turns it into a validated, provider-specific WAF rule - a state-driven agent graph that researches the vulnerability, maps the attack surface, composes the rule, then attacks its own output with an independent bypass judge and a false-positive prober before a human is ever offered a deploy. When a weakness survives, the system downgrades its own recommendation and records what it could not prove.
That last part is the thesis of this role.
An agent that touches production security must prove it works and declare what it couldn't prove. The security industry is about to be flooded with agentic claims nobody can verify. We intend to be the company that made verification legible - and that starts with holding our own agents to a standard we're willing to publish.
We're looking for an AI Tech Lead to own that standard across three surfaces:
The platform - Today each agent flow is close to a bespoke implementation. You'll turn our hard-won patterns into shared components, conventions, and infrastructure so the next agent is a week of work rather than a quarter - with evaluation, observability, and cost control built in rather than bolted on.
Enablement - our company's advantage compounds only if the whole company is AI-fluent, not just R&D. You'll raise that fluency everywhere - engineering, research, product, GTM - through tooling, patterns, and teaching.
The voice - You'll publish the methodology: how we benchmark agentic security output, how we model residual risk, what we learned failing. This is a category-defining position and we want it argued in public.
This is a hands-on lead role with no direct reports. Your authority comes from the quality of what you build and how clearly you explain it.
What You'll Do
Define and build our company's agent framework - shared components, orchestration patterns, tool interfaces, and conventions that make "how do I build an agent here" a five-minute answer.
Make evaluation a gate, not an afterthought: trajectory testing, golden datasets, offline replay, and per-dimension scoring that runs in CI. Land the release benchmark so promote / hold / rollback is a number, not a vibe.
Own agent observability end to end - the execution graph, tool invocations, intermediate reasoning, latency per step, and quality drift - including the external Temporal-orchestrated flows that are hardest to introspect today.
Own the economics: provider abstraction, model routing by task complexity, small-model substitution where it holds, cost attribution per flow. Thousands of CVEs through a labeling agent is a budget line, not a detail.
Drive latency and determinism in our production agent flows - tighter loops, early stopping, deterministic state machines over prose-in-prompt orchestration.
Raise the company's AI fluency. Build the internal tooling, skills, and playbooks that let every team - not just R&D - work AI-natively, and teach the judgment for when not to.
Partner with security research, product, and engineering leadership so agent capability and product roadmap actually converge.

The Applied AI Strategist helps customers turn complex business needs into practical AI solutions. This role combines business understanding, applied AI fluency, product thinking, and strong execution. You will work closely with customers and internal teams to understand problems, define solutions, guide implementation, and ensure the resulting systems deliver measurable value. You will bring structure to ambiguous challenges, make thoughtful trade-offs, and help move AI initiatives from concept to reliable production use.

What You'll do
Develop a deep understanding of customer workflows, requirements, constraints, and goals.
Identify and prioritize high-value opportunities for applying AI.
Translate ambiguous business needs into clear solution plans, requirements, and success criteria.
Design and validate solution concepts through prototypes, demonstrations, and structured evaluations.
Define prompt, context, workflow, and evaluation approaches for AI-powered solutions.
Coordinate stakeholders and maintain alignment throughout implementation.
Guide trade-offs across speed, quality, reliability, scalability, governance, and customer impact.
Identify and resolve risks or blockers that could affect successful delivery.
Measure solution performance, adoption, and business value.
Turn recurring customer needs and implementation lessons into reusable capabilities and best practices.

We're looking for an experienced AI Engineer to join our core team and design, build, and implement the autonomous AI agents that power our company's revolutionary approach to eliminating container vulnerabilities at the source.
Responsibilities:
Explore and prototype cutting-edge AI techniques to tackle our hardest challenges
Architect and develop autonomous AI agents that intelligently eliminate vulnerabilities during container image generation
Collaborate with product and engineering teams to bring AI-driven features from concept to production
Monitor and evaluate AI model performance, implementing feedback loops to continuously improve accuracy and reliability
Develop and maintain machine learning pipelines that process security data and generate actionable insights
Ensure AI systems are robust, reliable, and performant at enterprise scale.

In this role, you will learn and deeply understand the AI fundamentals and create tools that simulate our companys AI neural network processor implementation.
In addition, you will perform in-depth analysis and optimization to the simulator, to ensure simulator performance and accuracy for various companys processors architectures.
The project is using C/C++ OOP as main development language as well as python for AI neural network.
Development platforms are Linux and Windows based.
Responsibilities:
Taking part of innovative, agile, top-notch Software organization, designing Neural Network Processor (NPU) simulator in close collaboration with the architecture, algorithms, system and hardware teams.
Design and develop simulation software solutions allowing our company customers to run most advanced AI networks efficiently on our company NPU software simulation.

In this role, as Embedded Software Team Leader, you will lead a team owning a key part of our company's NPU AI software stack, a complex, multi-layered system spanning the Host (SDK, runtime, APIs, and UMD/KMD drivers) and the NPU IP (embedded firmware). This stack is at the heart of the company AI software offering, with deep impact on multiple hardware and software teams ability to execute state of the art LLM, Generative AI and Neural Networks with extraordinary performance. Reporting to the AI Software SDK Senior Team Leader, you will lead a team of creative and result oriented enthusiasts that invent, design, and implement software that enables smooth connectivity between the NPU and Host CPU.
Responsibilities:
Lead and manage the Embedded Software engineering team, ensuring high performance and productivity.
Own the design, development, and implementation of your component within our company's NPU AI software stack, aligned with the AI Software SDK group's technical strategy, architecture, and roadmap and its automated CI and release process.
Collaborate with cross-functional teams to integrate solutions. Provide technical guidance and mentorship to team members.
Ensure project timelines and deliverables are met.
Conduct performance reviews and support professional development of team members.
Collaborate globally with architects, HW teams, NPU teams, and other developer teams.

You will be a key contributor to our companys AI Graph Compiler software stack for NPUs, designing system-level execution flows for advanced neural networks, including LLMs.
The role focuses on L1/L2 memory management, data movement, and performance optimization, working closely with compiler and hardware architects.
What will you do:
Design and own key components of our companys AI Graph Compiler.
Develop and optimize neural network execution flows and memory management.
Enable complex AI workloads, including LLMs, and implement new NPU features.
Analyze performance bottlenecks and drive system-level optimizations.
Collaborate with compiler and hardware teams on HW-SW solutions.

In this role, you will build deep expertise in AI fundamentals, LLM and generative AI network architectures, and their underlying algorithms. You will continuously research emerging generative AI architectures across the industry, staying ahead of how the field evolves.
A central focus will be quantization - understanding, developing, and refining quantization methods and algorithms, and driving their deployment on our hardware to achieve maximum performance without compromising accuracy.
Responsibilities:
Explore and plan end to end network deployment on our company's hardware.
Own network accuracy measurement and improvement.
Develop the graph compiler's hardware-aware quantization tool.