לוח משרות דרושים NLP/Machine Learning משרה מלאה, לחיפוש זה נמצאו 104 משרות

We're looking for a Backend Engineer to help build and scale the Machine Learning Platform that powers how our company uses AI across the business. You'll be part of the ML Platform team, designing the infrastructure that lets our data scientists move faster, ship smarter, and operate with confidence in production.
We believe three things matter for every role at our company: drive to push through challenges, efficiency that keeps standards high while moving fast, and adaptability that lets you pivot with data and AI insights. These aren't buzzwords, they're how we actually work. Our AI-first approach isn't just a tagline either. We're building the future of insurance with AI at the center, and we need people who are genuinely excited to learn and grow alongside these tools.
In this role you'll
Design and build the foundational ML platform and AI agents to accelerate data science model delivery across all business units
Architect cloud-native microservices running on Kubernetes, using infrastructure-as-code to automate model deployment and management
Own the end-to-end ML lifecycle, covering training, testing, deployment, and real-time monitoring
Evaluate and choose the right tools and technologies based on workload demands and performance requirements
Collaborate with engineering, data science, and product teams to keep ML projects aligned with business goals
Identify and fix reliability, scalability, and performance gaps before they become problems.

As the Head of AI Security, you will be the ultimate authority and strategic leader responsible for securing our AI/ML infrastructure, data pipelines, and deployed models, you will bridge the gap between advanced data science and enterprise-grade security. You will design, implement, and oversee security frameworks that protect our proprietary AI technologies, multi-agent systems, and RAG architectures.
Youll Own:
our companys end-to-end AI security strategy across AI/ML infrastructure, data pipelines, LLMs, RAG systems, and autonomous AI agents
Secure-by-design frameworks and security architecture for AI-powered products and platforms
AI threat modeling, adversarial testing, red teaming, and vulnerability management programs
AI governance, internal security policies, and alignment with emerging global AI regulations and standards
Security tooling, automation frameworks, and continuous monitoring capabilities for AI environments
Cross-functional security leadership across Data Science, AI Engineering, Product, Infrastructure, and SecOps teams
Youll Solve:
Protecting AI systems against emerging threats such as prompt injection, model inversion, data poisoning, and training data leakage
Preventing sensitive customer and enterprise data from leaking into training sets or model outputs
Securing complex AI architectures including LLMs, RAG pipelines, and multi-agent systems at scale
Embedding security seamlessly into the ML lifecycle without slowing innovation
Building scalable AI security operations through automation, monitoring, and continuous adversarial testing
Navigating evolving AI governance, compliance, and regulatory requirements across global markets
Youll Impact:
Maintain our companys reputation as a leader in secure and trustworthy enterprise AI
Build the foundation that allows AI innovation to scale safely and responsibly
Strengthen customer trust by protecting highly sensitive conversational and business data
Create resilient AI systems that can withstand real-world attacks and emerging threat vectors
Drive a security-first AI engineering culture across the organization
Shape the future of AI security, governance, and MLSecOps in a hypergrowth AI company.

we are hiring a Software Engineer to help revolutionize the world of observability. Using cutting-edge AI-driven technology, we are redefining how organizations monitor, analyze, and optimize their systems-making observability more intelligent, efficient, and accessible than ever before. As we scale, we seek top talent to push the boundaries of innovation and shape the future of AI-powered observability. If you're passionate about solving complex challenges and building game-changing technology, join us in transforming how the world understands and interacts with data.



Responsibilities:

End-to-end development and ownership of products and features, from design to scalable and predictable production behavior

Solve diverse and complex problems in a high-scale, cloud-native environment

Collaborate with other engineers and product managers to improve product functionality, scalability, and performance

Design, develop, and maintain robust, secure, and efficient software solutions

Ensure high system reliability by implementing best practices in monitoring, observability, and automation

Review code, architecture, and data to identify and troubleshoot technical and performance issues

Work with AI/ML teams to integrate AI capabilities, including model monitoring, evaluation, and fine-tuning

Required Research Software Engineer
About the job
Our software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to our needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward. We're reimagining what it means to search for information - any way and anywhere. To do that, we need to solve complex engineering challenges and expand our infrastructure, while maintaining a universally accessible and useful experience that people around the world rely on. In joining the Search team, you'll have an opportunity to make an impact on billions of people globally.
Responsibilities
Write product or system development code.
Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
Implement solutions in one or more specialized ML areas, utilize ML infrastructure, and contribute to model optimization and data processing.

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.

Required Machine Learning Hardware Architect, Hardware, Software Co-Design, Cloud
About the job
In this role, youll work to shape the future of AI/ML hardware acceleration. You will have an opportunity to drive cutting-edge TPU (Tensor Processing Unit) technology that powers our most demanding AI/ML applications. Youll be part of a team that pushes boundaries, developing custom silicon solutions that power the future of our TPU. You'll contribute to the innovation behind products loved by millions worldwide, and leverage your design and verification expertise to verify complex digital designs, with a specific focus on TPU architecture and its integration within AI/ML-driven systems.
As a Machine Learning Hardware Architect within the Co-design team, you will serve as a technical lead bridging model architecture innovation and next-generation hardware design. Operating at the highest levels of AI research and engineering, you will define the goal and architectural roadmap for our future machine learning serving and training capabilities. You will guide the integration of ML research such as massive-scale foundation models with advanced silicon architectures to create industry-leading, high-performance, and power-efficient accelerators.
Responsibilities
Define and drive the technical roadmap and architecture for the hardware/software stack to ensure exceptional performance for ML models. Act as the technical liaison across research, software, and hardware teams, steering model architecture innovation to maximize scaling, quality, and hardware efficiency.
Architect next-generation configurable simulation frameworks and performance models, setting the organizational standard for evaluating complex microarchitectural decisions. Drive high-stakes choices regarding Power, Performance, Area (PPA) and buildability for future chip and system architectures, expertly balancing long-term technological trends with strict product delivery timelines.
Guide system-level performance analysis across highly distributed ML systems, innovating new methodologies to optimize and balance compute, memory bandwidth, and inter-chip network requirements. Their leadership will directly shape the future of high-performance AI infrastructure and hardware-software co-design.
Manage cross-functional partnerships across hardware, compiler development and ML teams.

We are seeking a Senior AI Engineer to join core product organization, where you will design, build, and scale next-generation AI systems powering real-world cybersecurity use cases across our diverse product portfolio (Posture, Detection, and CTI). This role focuses on developing production-grade systems leveraging LLMs, advanced machine learning, and agent-based architectures.
You will join a team within Cyber R&D organization-leading the companys core product portfolio- while driving AI innovation and establishing engineering best practices across the domain. The team focuses on building and optimizing large-scale AI systems, including LLM-based solutions and advanced multi-agent workflows, working closely with data scientists and researchers to bring ideas into production.
The Responsibilities:
Design, build, and own end-to-end AI solutions- from data collection and preprocessing to model training, evaluation, and production deployment.
Optimize systems for performance, scalability, and reliability in production environments.
Collaborate closely with product, design, and engineering teams to identify and deliver AI-driven capabilities that address real customer needs.
Stay up to date with emerging AI/ML technologies, frameworks, and best practices, and apply them where they create real impact.
Work across the stack, contributing to backend systems and data pipelines that support large-scale AI applications.
Troubleshoot and resolve complex system issues, including performance bottlenecks, race conditions, and memory-related challenges.
Approach problems with a strong analytical mindset, delivering robust solutions while contributing to a high-performing, collaborative team environment.

We are seeking an experienced, strategic Senior Product Manager to lead the discovery, definition, and delivery of impactful product initiatives across platform. You will own the product vision and roadmap for your domain, balancing customer needs, technical feasibility, and long-term business goals. This role requires exceptional product thinking, leadership, data-driven decision making, and close collaboration with engineering, design, and go-to-market teams to drive innovation in the CPS space.
Responsibilities:
As a Senior Product Manager, your impact will be:
Product Discovery & Delivery: Empower and guide the product and engineering teams to discover, design, and build viable solutions to complex problems that delight customers.
Lifecycle Ownership: Own the end-to-end product lifecycle from strategy to execution-converting high-level market feedback, AI capabilities, and technical requirements into successful, scalable releases.
Strategic Roadmap: Play a key role in driving the product roadmap planning and prioritization process, synthesizing diverse inputs into a clear, forward-looking product definition.
Go-To-Market Partnership: Partner closely with Sales, Marketing, and Customer Success to drive product adoption, enable global teams, and support strategic GTM efforts.
Cross-Functional Collaboration: Work across horizontal R&D teams to ensure scalable, unified, and cohesive platform delivery.

Were hiring a Validation data Scientist to join a leading Fintech company and work on the validation of Machine Learning -based credit models. Contract: Temporary position until the end of the year, with an option to extend. Location: Tel Aviv, on the train line. Work model: Hybrid - 2 days from home per week.
?What will you do Validate ML-based credit models, assessing performance, data quality, robustness, and conceptual soundness. Challenge model assumptions and propose alternative approaches when needed. Analyze model strengths and weaknesses and provide practical recommendations for improvement. Generate insights and analyses to support data -driven decisions. Prepare thorough model validation documentation in accordance with regulatory standards. Support ongoing monitoring and improvement of predictive models.

We are looking for a Principal MLOps Engineer with a deep focus on ML Platforms and Infrastructure to join our Data & AI group at Cortex Research. Our team is responsible for designing, building, and scaling the foundational MLOps and LLMOps platforms that power both our Data Scientists and Security Researchers. You will architect the high-performance core infrastructure that enables these roles to build, train, and deploy advanced AI systems-ranging from optimized Small Language Models (SLMs) to complex agentic workflows and RAG systems. If you are passionate about building scalable compute platforms and automating the full ML lifecycle to solve complex data and security challenges, we want to hear from you.
Key Responsibilities
Scale Distributed Training: Design and optimize infrastructure for training and fine-tuning LLMs and SLMs, leveraging distributed GPU workloads, efficient clustering, and compute optimization.
Automate the ML Lifecycle: Architect robust, automated pipelines for continuous training (CT) and deployment (CD) of models, ensuring a seamless flow from raw data collection to production environments.
Build Model Infrastructure: Own the serving architecture for LLMs/SLMs, balancing latency, throughput, and GPU utilization under production traffic.
Implement Advanced Monitoring: Establish comprehensive observability systems to monitor live model performance, data drift, and computational metrics, feeding insights back into the automated training loops for continuous improvement.
Collaborative Architecture: Partner closely with data scientists and security researchers to productize complex model architectures and streamline their workflows, while collaborating with our DevOps team to integrate with core cloud infrastructure.

As a Senior Software Engineer specializing in the AI & ML ecosystem, you'll be a core contributor owning and evolving critical parts of AI ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Engineers and Data Scientists to harness speed and scale in the frameworks they already use.

We're looking for someone who has firsthand experience as an AI & MLEngineer or Data Scientist. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why.

You'll own the full lifecycle of key AI/ML integrations, driving architecture, performance, and feature direction across AI & LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with as a vector store, ML feature stores, and LLM-powered data applications.

columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real: building the robust, production-ready connectors that make the natural choice when data practitioners design their next-generation AI and data systems.

What you'll do
Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design
Drive the AI/LLM integration strategy: designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications
Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback
Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities
Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows

Required Senior Agentic Systems Engineer
Israel - Raanana
We dont limit our challenges. We challenge our limits. Always. Were ambitious. Were game changers. And we play to win. We set the highest standards and execute beyond them. And if youre like us, we can offer you the ultimate career opportunity that will light a fire within you.
So what is the role all about?
The Senior Agentic Systems Engineer is a hands-on engineer who designs, builds, and operates AI-powered tools and workflows for engineering teams.
Were looking for builders who are not only users of AI tools, but can design autonomous agentic systems, optimize model usage, create reusable agents and workflows, and orchestrate complex multi-step work across multiple tools and agents.
Youll be a one-person-army builder: able to understand a problem, define the approach, build the backend, create a simple UI when needed, integrate APIs, connect agents, automate workflows, and deliver working tools quickly.
How will you make an impact?
Build centralized AI-powered tools for engineering teams.
Create and maintain agents, skills, hooks, MCP servers, plugins, and workflow automation.
Design autonomous agentic systems for internal engineering use cases.
Build multi-step AI workflows across code, documentation, Jira, GitHub, CI/CD, and internal systems.
Build tools for PR review support, test generation, documentation updates, spec creation, codebase understanding, and developer productivity.
Learn from local teams and convert successful solutions into reusable centralized assets.
Integrate AI tools with GitHub, Jira, Confluence, CI/CD, internal portals, and engineering systems.
Optimize AI workflows for cost, latency, quality, and reliability.
Apply token economy thinking when designing agents, prompts, MCP usage, RAG flows, and tool integrations.
Contribute to the internal AI marketplace and reusable asset catalog.
Work directly with engineering teams, collect feedback, and iterate quickly.

A significant part of the role involves building end-to-end integration flows and tests, particularly around token generation pipelines and system orchestration, as well as contributing to intelligent system behavior such as hardware selection and execution strategies. The role also includes developing system-level logic in Python for multi-tenant management, caching strategies, and service lifecycle management across the platform.

***This is not a Data Science position***

Responsibilities:

Build and maintain end-to-end integration flows across the AI inference pipeline (serving, orchestration, APIs, and infrastructure)
Design, implement, and optimize LLM inference workflows, including prefill and decode stages
Improve system performance with focus on throughput, latency, and interactivity
Write production-grade components in Python and integrate them into the broader system
Contribute to system-level logic such as smart hardware selection and execution strategies
Integrate models (open source and custom), services, and APIs into cohesive, reliable end-to-end application pipelines

We are looking for a Senior Software Engineer to help build and optimize large-scale, high-performance GenAI infrastructure and inference systems on Kubernetes.

As AI workloads increasingly move toward Kubernetes-native infrastructure, we are building systems that support distributed inference, performance optimization, reliability, observability, and production-grade deployment at scale.

This role is ideal for an engineer who can reason deeply about systems, performance, tradeoffs, and reliability, and who is comfortable owning difficult technical decisions end-to-end.

You will work across inference serving, distributed systems, optimization, and Kubernetes-native AI infrastructure.

What Youll Do

Build and optimize high-performance Kubernetes-native GenAI inference systems
Work with modern inference stacks such as vLLM, SGLang, TensorRT-LLM, and related tooling
Work with Kubernetes-native distributed LLM inference frameworks such as llm-d and NVIDIA Dynamo
Design and implement optimization algorithms and performance improvements
Improve reliability, observability, deployment, and operational maturity of AI systems
Make architectural decisions and take ownership of technical outcomes
Collaborate with a small, senior engineering team focused on performance and production quality

We are seeking a talented and motivated Data Scientist for a temporary position to support our growing data, analytics, and AI automation needs. This role focuses on turning large volumes of data into models, insights, and intelligent automation - combining classic data science (statistical analysis, feature engineering, machine learning) with the emerging Agentic AI stack (LLMs, MCP, agent orchestration). You will work closely with data engineers and internal teams to prototype and productionise models, build LLM-powered agents and workflows, and support the integration of AI capabilities across the organization.

The ideal candidate is passionate about data and AI, comfortable navigating complex systems, and excited by the opportunity to operationalize AI within a modern enterprise environment. We value curiosity as much as experience: we are looking for someone eager to show what they know, and equally eager to keep learning in a field that moves fast.


Responsibilities
Explore, analyze, and model large volumes of structured and unstructured data in Python, from exploratory analysis and feature engineering through to model validation and communication of results.
Design, train, evaluate, and deploy machine learning models, and monitor their performance, accuracy, and drift in production.
Build and orchestrate Agentic AI solutions - LLM-based agents, RAG pipelines, prompt design, and evaluation frameworks - to automate data quality checks, investigation, and reporting workflows.
Integrate models and agents with internal systems and data sources using MCP servers and clients, and workflow automation platforms such as n8n.
Write efficient and maintainable SQL queries to support analysis, reporting, and data exploration needs.
Collaborate with data engineers to productionise models and agents: reliable data flows, logging, alerting, and performance tuning.
Participate in the development of internal tools and dashboards that make data and AI capabilities accessible across the organization.
Share findings with the team and help evaluate emerging AI tooling as the ecosystem evolves.