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

Were looking for a Founding AI Engineer to build and define the research function at Upriver from zero. This is a rare opportunity to work on some of the hardest unsolved problems in applied AI and agents, with massive ownership, visibility, and room to grow into leadership quickly.

What Youll Do
Lead the AI domain at Upriver: Take ownership of our agent architecture and help define its next phases, standards, and direction.
Solve hard, unsolved problems: Work on challenges in AI agents, reasoning over complex systems, and autonomous decision-making that dont yet have clear playbooks.
Build production AI: Design, implement, evaluate, and deploy AI systems used by real customers.
Be outward-facing: Publish technical blogs, benchmarks, and papers; represent Upriver in the AI and data community.
Move fast with autonomy: Own problems end-to-end with minimal guidance, working directly with founders.
Deliver real customer value end-to-end: take features from idea to production, see them used by customers, and iterate based on real-world impact.

Required AI Algorithm Engineer - Vision Localization Group
About the team:
Our vision Localization group is responsible for accurately localizing the vehicle on a high-definition 3D map with centimeter-level precision using surround camera systems to enable autonomous driving. Our work combines advanced geometric reasoning, scene understanding, and large-scale mapping to solve one of the core challenges of autonomous vehicles in complex real-world environments.
The role involves developing algorithmic and deep learning solutions that integrate mathematical optimization, computer vision, and 3D geometry. We work with modern neural network architectures including Transformers, GNNs, and CNNs to solve problems involving scene understanding, object association, and precise spatial reasoning in dynamic environments. The HD map contains rich geometric and semantic information that is critical for safe driving and cannot always be inferred reliably from images alone under all visibility conditions or viewpoints. In addition to achieving highly accurate localization, the team is also responsible for estimating map relevance and consistency with the current world state to ensure safe and reliable autonomous driving.
What will your job look like:
Develop state-of-the-art localization algorithms for autonomous driving using multi-camera surround vision systems.
Design and implement deep learning models based on Transformers, Graph Neural Networks, and Convolutional Neural Networks.
Solve challenging problems involving 3D geometry, scene understanding, object matching, and spatial reasoning.
Combine learning-based methods with classical optimization and geometric algorithms.
Own the entire lifecycle from prototyping to deployment.
Analyze real-world driving data and improve system robustness under diverse environmental and visibility conditions.
Drive innovative solutions for high-precision localization at production scale.

As an AI Engineer, your primary focus will be delivering working features while adopting a Builder Mindset to shape the future of software development. You will leverage cutting-edge GenAI tools (such as Claude Code and others) to accelerate development, streamline workflows, and bring high-quality, production-ready features to life.
In this role, you will work closely with PMs, designers, and fellow engineers across frontend, backend, and data domains. You will use AI to tackle complex debugging, conduct research, and eliminate repetitive tasks-continuously improving your productivity while delivering functional, impactful features within your pod.
Responsibilities:
Feature Delivery: Own and deliver working features from end to end, contributing production-ready code with guidance and driving tangible product value.
AI-Accelerated Development: Utilize GenAI tools (e.g., Claude Code) to accelerate feature development, improve personal productivity, and optimize daily workflows.
Problem Solving & Debugging: Leverage AI for rapid debugging, technical research, and reducing repetitive manual work during feature delivery.
Cross-Domain Collaboration: Build a solid understanding across product, frontend, backend, and data domains, collaborating effectively with PMs, designers, and engineers.
Context & Documentation: Document technical decisions clearly and contribute to building shared team context and knowledge bases.
Continuous Learning & Adaptability: Learn the tech stack quickly, adapt smoothly to new tools/workflows, and actively apply feedback to grow as a builder.
Pod Reliability: Act as a dependable, collaborative contributor within your pod, helping drive team momentum and execution.

This role owns the AI-native GTM engine: the systems that find, enrich, reach, and route prospects and customers, and the workflows that create new opportunities for the company. You build revenue systems with AI tooling, then run them against pipeline targets. The scope is wide on purpose. You own the motion and the measurement, so theres no gap between what you build and what you can prove.

What youll own
Outbound engine. Multi-channel outbound end-to-end: list building, segmentation, messaging, sequencing, launch, and A/B iteration across email and LinkedIn. AI-driven personalization at scale, moving prospects from first touch to booked meeting with minimal manual input from sales.
Inbound conversion. Everything between the first visit and the booked meeting: on-site conversion paths, forms, chat, scoring, routing, and speed-to-lead. Instrument every step, find where intent leaks, and close the gap with automation instead of headcount. No inbound lead should sit waiting for a human to qualify it.
CRM truth and attribution. The measurement layer under everything else. If outbound, inbound, AEO, and content cant be attributed, none of it can be optimized or defended. Own data quality across HubSpot and Salesforce.
Data and enrichment. Prospect identification and signal tracking: enrichment waterfalls, buying signals, social listening, and clean push architecture into the CRM.
AI agents and automation. Claude is a teammate here, not a chatbot. Build the agents, skills, and workflows that encode our GTM playbooks and kill anything manual.
AI content and discovery. Build the writing agents that produce inbound at volume: programmatic pages, competitor comparisons, persona and vertical landing pages, localized variants, and one asset turned into fifteen across channels. AEO is the other half of this. Buyers now start in ChatGPT, Claude, Perplexity, and AI Overviews, not on a results page, so the content has to be built to be retrieved and cited: structured content and schema, presence on the review sites and communities models pull from, and a technical layer that makes our legible to crawlers.
Customer expansion. The signal engine that finds revenue inside the base: segmentation, expansion triggers, and routing each account to the right owner or the right sequence at the right time.
The stack itself. The GTM tech stack end-to-end: evaluate, buy, integrate, and kill tools as the motion evolves, and build tools with direct impact on revenue when nothing off the shelf does the job.

we are looking for a AI Engineer.
You will build the shared platform layer that makes agentic AI extensible, measurable, and self-improving. This is the foundation beneath everything: the orchestration engine, the skills registry, the evaluation harness, the guardrails system, the memory service, the integration gateway, and the tracing infrastructure - consumed by both the real-time conversational and offline video generation runtimes.
Your work is what separates it works for one customer with engineering involvement from it works for many customers across many domains without bespoke engineering per customer. You build the machinery once; customer-facing teams and eventually customers themselves extend it.

we are looking for a Senior AI Engineer - Exploration & Prototyping.
That is this role. You take an open question, run a focused spike, and come back with numbers and a recommendation. You read the source of the frameworks you evaluate rather than trusting their marketing. You build prototypes to settle arguments.
You do not own a subsystem and you do not ship to customers, which is exactly what protects the work: exploration inside a delivery team always loses to the sprint. You sit alongside the platform, research, and forward-deployed teams, you borrow their context freely, and your output is evidence they can act on.
You will be trusted with real influence early. The recommendations you write become the architecture other people build against, so the bar is not a working demo but a defensible conclusion, including the ones that say no.
What Youll Do:
Run technical spikes that close open decisions, covering agent orchestration frameworks, real-time transport, memory protocols, agent interoperability standards, LLM selection and routing, evaluation harnesses, and the production library and stack choices underneath all of it.
Build prototypes to de-risk, standing up something real quickly, proving or disproving the thing in question, and moving on without becoming attached to the code.
Read and evaluate unfamiliar codebases, going into the source of a candidate framework to find out whether it can actually support what we need rather than what its documentation implies.
Design the measurements that make a decision defensible, building the harness, running the comparison, and reporting latency, cost, and failure behavior honestly.
Own build-versus-adopt recommendations for platform infrastructure, frameworks, and libraries, including a clear statement of what it would cost to be wrong.
Write the recommendation down. Every spike ends in a short, decisive document another engineer can act on, with the evidence, the rejected options, and the reasoning behind the call
Hand off cleanly, transferring what you learned to the team that will own the capability in production, and staying available while they pick it up.
Track the landscape across agentic infrastructure, real-time frameworks, and adjacent AI tooling, and bring forward the things that genuinely change what we can build.

We are looking for an Applied AI Engineer to join the MECI Interfaces team within the Software Engineering organization. The MECI Group enables our algorithmic development flow by building scalable CI, build, test, and AI-driven tooling that supports the full development lifecycle. Our platforms allow us to scale efficiently and increase development velocity, while maintaining high standards of reliability, collaboration, and developer well-being.
As an Applied AI Engineer, you will be at the forefront of integrating Large Language Models (LLMs) and agentic workflows into our internal platforms. Your primary goal will be to solve complex problems across the Software Development Life Cycle (SDLC) by building intuitive, AI-powered tools that directly support our developers and internal users. This role offers the unique opportunity to both expand the intelligence of our existing developer tools and architect entirely new workflows from the ground up in a highly technical, fast-paced environment.
What will your job look like?
Design, develop, and deploy AI-driven internal tools and agentic workflows that accelerate the SDLC and reduce friction for our developers.
Expand the capabilities of existing platforms by embedding smart, context-aware LLM features.
Identify high-impact bottlenecks across the engineering organization and build zero-to-one AI solutions to solve them.
Architect robust orchestration layers around LLMs, focusing on practical implementations of custom system "skills," tool calling, Model Context Protocol (MCP) integrations, memory management, and Retrieval-Augmented Generation (RAG).
Maintain a framework-agnostic approach, rapidly evaluating and adopting the most effective AI models, APIs, and open-source techniques as the landscape evolves.

We are looking for a Hands-On Agentic AI Engineer (Information Systems Team) to join a new team focused on applying AI agents and intelligent workflows to improve business processes across the organization.

The ideal candidate is someone who enjoys building real-world AI-driven solutions end-to-end - writing code, designing architectures, building prototypes, solving implementation challenges, and working directly with business stakeholders to turn process opportunities into working AI solutions.



Responsibilities:

Partner directly with business teams to identify automation and optimization opportunities
Design and implement agent-based AI workflows to automate internal processes end-to-end
Design and build LLM-powered tools (agents, workflows, copilots)
Develop RAG pipelines, integrate multiple data sources, and build intelligent automation flows
Deep-dive into company data - validate quality, uncover gaps, and ensure AI solutions are built on solid foundations
Take solutions from idea → prototype → production
Governance, Reliability & Security
Ensure AI workflows comply with security, privacy, and compliance requirements
Implement guardrails, approvals, logging, and human-in-the-loop mechanisms where needed
Monitor AI performance, errors, hallucinations, and drift
Collaboration & Enablement:
Partner with business owners and IS teams to identify automation opportunities
Translate business requirements into AI-driven solutions
Document AI flows, decision logic, and operational runbooks
Educate internal teams on AI capabilities and limitations

We are looking for a hands-on AI Platform Team Lead to build and lead the team behind this platform: a high-throughput, low-latency engine that runs GPU-based models, from MMBERT-style models to LLMs, together with CPU-based heuristics and security logic.
This is a core infrastructure role for someone who wants to own the runtime layer of AI security at scale: performance, reliability, orchestration, GPU efficiency, and production-grade execution in the traffic path.
The team will also own the model lifecycle required to take AI security algorithms from research to large-scale production, working closely with research and algorithm teams.


Responsibilities
Build and lead Catos AI Platform team: hiring, mentoring, architecture, technical direction, and execution.
Own the AI security runtime platform for high-throughput, low-latency inline security decisions across Catos global cloud and PoPs.
Design the orchestration layer for running GPU models, CPU heuristics, and security logic as one production engine.
Own production readiness: observability, SLOs, autoscaling, reliability, rollout, rollback, and operational health.
Own the model lifecycle platform: registry, versioning, deployment, monitoring, and safe production rollout.
Work closely with research and algorithm teams to productionize AI security models and algorithms at scale.
Define the long-term platform strategy for AI runtime and model serving at Cato.

We are looking for a Senior Data Scientist / Applied AI Researcher with deep expertise in NLP and LLM-based systems, and the ability to operate across the full lifecycle, from problem formulation and research design to production deployment and real-world iteration.

You wont be working on isolated experiments. Youll be part of a multidisciplinary team that owns the system end-to-end and is responsible for how it behaves under real production traffic.

This role requires strong scientific discipline and the maturity to balance innovation with practical constraints.




What youll do
End-to-End Ownership: Lead AI security solutions from problem formulation and research design through model development, evaluation, and production deployment. Ensure solutions are measurable, reliable, and effective under real-world traffic.
Drive Technical Direction: Take responsibility for ambiguous AI security problems, propose thoughtful approaches, and drive structured experimentation and deep model analysis.
Build for Scale: Design systems that are not only accurate, but scalable, efficient, and robust.
Staying Updated: Continuously explore advances in NLP and LLM-based systems, and critically assess what is worth integrating into a production environment.

We're hiring an exceptional Applied AI Scientist to join our team!
In this role, you'll design and deploy enterprise-grade AI agents that drive real business impact across core functions - Sales, Marketing, Customer Success, Customer Support, Risk, and Finance. You'll be building agentic systems that execute complex, multi-step business workflows end-to-end. The ideal candidate is a hands-on builder with deep AI expertise who thrives on turning complex business problems into reliable, production-grade AI systems.
In This Role, You Will Be Responsible For:
Enterprise AI Agent Development: Designing, building, and deploying AI agents that automate and augment high-value workflows across business functions - including lead qualification and outreach (Sales & Marketing), churn reduction(Customer Success), and Ticket deflection (Support).
Agentic Architecture & Orchestration: Owning the end-to-end agent stack - from tool use, memory management, and multi-step planning to human-in-the-loop escalation patterns, guardrails, and audit trails suited for an enterprise fintech environment.
Knowledge Systems: Building and maintaining RAG pipelines that ground agents in Tipalti's proprietary knowledge - product documentation, customer data, financial records, and internal playbooks - using vector databases, hybrid search, and re-ranking.
Evaluation & Continuous Improvement: Defining agent evaluation frameworks that measure task completion, accuracy, hallucination rates, latency, and business impact - then iterating on agent behavior based on real usage data and stakeholder feedback.
Cross-Functional Collaboration: Partnering with stakeholders across the company to define agentic use cases, translate business requirements into technical features, and deliver products that create measurable ROI - from data preprocessing through product deployment.

Were looking for a hands-on AI Engineering Tech Lead to define and drive AI-native engineering practices, lead the architecture and development of scalable, intelligent systems, and embed AI across the software development lifecycle. This role involves providing technical leadership, identifying and solving architectural challenges, and collaborating cross-functionally to deliver high-impact, production-grade solutions.


Responsibilities:
Define and codify AI-native engineering practices, patterns, and guidelines to elevate the software development lifecycle.
Provide technical leadership and mentorship, fostering an AI-first engineering culture across teams.
Architect and lead the development of AI-native system frameworks that emphasize modularity, extensibility, and adaptive scalability.
Identify architectural risks, systemic bottlenecks, and long‑term scalability concerns - and proactively drive solutions.
Collaborate closely with Product, Customer success, Security and Business stakeholders to ensure technical solutions deliver real business impact.

our company offers hope to patients suffering from rare and severe diseases by forming partnerships with emerging biotech companies to accelerate access to highly innovative therapies in international markets. As the creator and leader of the global partnership category in the pharma industry, we strive to be Always Ahead and work relentlessly to bring therapy to patients in need, no matter where they live. Our values are at the core of every action we take, and we are committed to going above and beyond for the benefit of the patients we serve. We are a dynamic, fast-paced company operating in over 30 countries on 5 continents. We are looking for out-of-the-box thinkers, people who are passionate, caring, agile, and adaptive, to join us on our mission. If you want to make a difference in people's lives, we invite you to join us! We are looking for a professional Senior AI Enablement to drive AI and business transformation initiatives, combining strategic thinking, execution discipline, and strong cross-functional stakeholder management. This role will play a key part in shaping and driving AI-enabled transformation across the organization, working closely with business, product, AI, and IBT teams to translate strategic priorities into clear execution plans, adoption frameworks, and measurable business impact. The role will be responsible for driving transformation execution governance, including initiative prioritization, business process understanding, dependency management, meeting outcomes, follow-up cadence, risk tracking, and cross-functional accountability across strategic initiatives.
Responsibilities:

* Drive AI and business transformation initiatives from strategy into structured execution.
* Work closely with business, product, AI, and IBT stakeholders to shape AI-driven capabilities and adoption processes.
* Lead transformation execution governance, including planning, prioritization, follow-up, dependency management, and execution rhythm.
* Translate business priorities into actionable plans, clear workstreams, and measurable outcomes.
* Define KPIs, ROI models, and cost-related considerations to support decision-making and value realization.
* Identify risks, gaps, dependencies, and blockers, and ensure timely escalation and resolution.
* Build and implement transformation processes, governance structures, and execution frameworks.
* Support senior stakeholders with clear communication, structured updates, and presentation materials.
* Drive cross-functional alignment and accountability across multiple teams and initiatives.
City:
Petah Tikva

CodeValue is looking for a hands-on Senior AI Engineer with real-world experience building and deploying LLM-based and Agentic Systems to production. You will be responsible for taking AI solutions from concept to reliable, scalable production systems, working across AI, backend, cloud, and infrastructure. Responsibilities
* Design, develop, and deploy LLM-based and Agentic Systems for production environments.
* Take AI/Agentic solutions from PoC and concept stages to stable, scalable, and reliable production systems
* Develop backend services and AI solutions using Python
* Work hands-on with agentic frameworks such as LangGraph, CrewAI , or similar/custom frameworks.
* Deploy and operate AI systems in cloud environments
* Troubleshoot complex system issues and improve observability, reliability, and performance
* Collaborate with development, DevOps, security, and other technical teams.

We are looking for a strong Backend Software Engineer to bridge the gap between our Machine Learning research team and our enterprise production systems. You will act as the technical backbone for our ML Scientists - by advising, designing and implementing the production facing features. If you are a backend expert who wants to solve complex system architecture challenges and dive into the world of ML platforms & Agentic LLM pipelines, this is the role for you - An exciting role collaborating with ML science team, data/infra team and DevOps to drive real customer impact.



As a ML Engineer, you will:



Lead ML delivery: transforming research output (code, models, ideas) into robust, scalable, low-latency microservices in production

Help architect e2e solutions to real customer pains ranging from ingestion, integration, ETLs, DB design up to low-latency services

Design, build, and maintain automated workflows for ML models, including auto-trains, benchmarking, testing, performance gating, and production deployment.

Tackle complex backend challenges: optimizing API response times, managing database connectivity and concurrency at scale, balancing accuracys drive for complex questions with the business needs of fast responsiveness by making hard technical trade-offs between customer gains and business costs.

Design and optimize data pipelines and ETL processes, connecting our Snowflake data warehouse to our training environments.

Work within our existing ML infrastructure (Kubeflow, MLflow, KServe) to ensure smooth model lifecycles and performance monitoring.

Collaborate closely with ML Scientists, guiding them on software engineering best practices without slowing down their research.

Monitor and optimize production models for performance, cost efficiency, availability, and observability.