לוח משרות דרושים NLP/Machine Learning בתל אביב יפו

We are looking for a visionary AI Lead to build our internal AI platform and architect adaptive intelligence systems that serve as a dedicated security architect for each of our customers. You will lead a team of AI Researchers and Engineers to move beyond simple integrations and build true autonomous security solutions.
What You Will Build
Autonomous Workflows: Build agents that execute security workflows end-to-end, automating detection, investigation, and response processes.
Runtime Defense: Productize research into active runtime guardrails and defensive control mechanisms.
Internal AI Platform: Build the shared infrastructure for models, training, and evaluations to enable safe, scalable AI features across the organization.
Adaptive Intelligence: Oversee the development of models that learn from customer-specific environments to generate high-signal, personalized insights.
Responsibilities
Lead the AI Research Group, managing AI Researchers, Engineers, and DevOps.
Own the intelligence engine end-to-end, delivering next-generation analytics and contextual visualization features.
Drive the strategic "build vs. partner vs. buy" decisions for cloud prevention and AI controls.
Collaborate with the Threat Team to develop AI-driven threat detections against emerging attack vectors and novel AI threats.

we are seeking an experienced and visionary Head of Research to lead, shape, and advance the future of AI innovation across the organization. In this role, you will own the research strategy end-to-end - from long-term roadmap design to hands-on exploration, and guide a multidisciplinary team of NLP research engineers and linguists toward breakthroughs that elevate our core medical coding engine.

You will play a key role in the company, balancing exploratory research with high-impact product delivery. You will partner with Product, Engineering, and executive stakeholders to identify strategic opportunities, drive prioritization, and translate research capabilities into real-world outcomes at scale.

Responsibilities:

Manage and mentor a multidisciplinary research team while driving both technical excellence and personal growth.
Define the long-term research roadmap aligned with product and business objectives.
Lead the full research lifecycle, from ideation and feasibility studies to experimentation, evaluation methods, and production-ready delivery.
Evaluate and introduce new research capabilities and emerging trends such as LLM-based reasoning, semantic/graph modeling, hybrid rule-learning, and other frontier techniques.
Continuously track scientific trends to maintain a state-of-the-art research practice.
Collaborate closely with Product and Engineering teams to translate domain challenges into algorithmic solutions with measurable product impact.
Represent the Research function at leadership forums, communicate priorities and outcomes, and align stakeholders around research strategies and milestones.

As part of this role you will, design, develop, and deliver high-quality, scalable, reliable, and extensible products. Work within our multidisciplinary R&D teams (Software Engineers, Linguists and NLP Research Engineers) and with the Product Management team.

This is an exceptionally exciting time to join a real AI company - when the very foundations of NLP are being redefined in front of our eyes. youll be at the forefront of that transformation, helping to shape the next generation of AI systems that are not only cutting-edge but also deeply impactful.

As a Research Engineer, you will design, develop, and implement algorithms and models that will enhance the products capabilities, performance, reliability, and scalability. Together with our algo and engineering team members, you will work closely with our product team members to implement algorithms that can be used to drive our business forward as well as the frontier of software development AI technologies.

Responsibilities:

Design, develop, implement and test algorithms and AI-empowered solutions for various product features and applications
Collaborate with cross-functional teams to understand business needs and translate them into algorithmic solutions
Perform research on emerging trends, algorithms, and cutting-edge technologies, and identify ways to incorporate them into our products pragmatically
Propose new and innovative solutions to complex problems and lead the development of algorithms in those areas
Design, cleanse, and utilize benchmarks throughout the process of the algorithm development

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
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.

We are looking for a Research Team Lead to establish and lead a cross-product research team focused on horizontal, high-impact initiatives that influence multiple offerings.
Unlike product-embedded research roles, this team drives foundational and strategic research projects across networking, security, identity, automation, and AI-driven capabilities. Examples include large-scale reasoning systems, autonomous policy frameworks, cross-domain detection and response concepts (xOps), and platform-wide intelligence capabilities.
You will combine deep technical expertise with strong leadership and execution skills -identifying impactful research directions, building a high-performing team, and turning advanced research into real platform capabilities used by thousands of customers worldwide.
Responsibilities
Technical Vision & Strategy:
Define and execute the roadmap for cross-product research initiatives.
Identify high-leverage research opportunities that impact multiple domains and products.
Drive long-term architectural thinking and influence platform evolution.
Balance innovation, experimentation, and production-readiness.
Team Leadership:
Recruit, mentor, and grow a multidisciplinary team of researchers (AI, data, algorithms, networking, security).
Establish high standards for research rigor, experimentation methodology, and engineering quality.
Foster a culture of ownership, collaboration, and technical excellence.
Research & Execution:
Lead complex, ambiguous research initiatives from ideation through validation and productionization.
Design large-scale experiments and validation methodologies using our data platform.
Drive innovation in areas such as:
Autonomous policy systems
Cross-domain detection and response frameworks
Large-scale reasoning and decision systems
Data-driven optimization and automation capabilities
Ensure research outcomes are measurable, scalable, and aligned with business impact.
Cross-Functional Collaboration:
Work closely with Product, Engineering, Architecture, and Product Research teams to translate research into shipped capabilities.
Provide technical guidance and influence cross-organizational decisions.
Act as a bridge between exploratory research and production systems.
Communication & Influence:
Present research findings and strategic recommendations to senior leadership.
Produce clear technical documentation, design proposals, and internal position papers.
Represent Platform Research as a center of excellence for cross-product innovation.

Were hiring an AI Backend Engineering Manager to guide and grow a high-impact ML team driving AI-powered innovation across B2B SaaS platform. Youll lead the design and delivery of AI solutions while mentoring engineers and setting the technical direction for AI-first development at scale.
This is a leadership role with a balance of hands-on engineering and team management, perfect for someone who thrives on solving technical challenges, inspiring a team, and shaping the future of AI in fintech automation.
What You Will Do:
Lead & Mentor: Manage, mentor, and grow a team of AI/ML/Backend engineers, fostering technical excellence and career development.
Set Technical Direction: Define the ML strategy, ensuring best practices in architecture, frameworks, and operationalization.
Build and deploy AI-based solutions: Oversee the development and deployment of GenAI/LLM-powered solutions that address real-world challenges across products.
Scale & Operationalize: Establish scalable ML infrastructure, CI/CD, observability, and data pipelines for high-availability production systems.
Collaborate Cross-Functionally: Partner with product managers, engineers, and business stakeholders, clearly communicate progress, challenges, and outcomes.

we are looking for an experienced Data Science Manager to lead and grow our Data Science team. This is a managerial role focused on leading a team of Data Scientists while driving the strategy, execution, and delivery of impactful AI and ML initiatives across our SaaS platform.
You will be responsible for both people leadership and technical direction - ensuring high standards of execution, mentoring team members, and aligning data science efforts with product and business goals. This role requires a strong hands-on background combined with proven team management experience.
What You Will Do:
Lead, mentor, and manage a team of 7-10 Data Scientists, fostering a culture of ownership, excellence, and continuous learning.
Own the teams roadmap, prioritization, and delivery of AI initiatives.
Provide technical and architectural guidance across projects.
Drive end-to-end execution of data science solutions - from problem definition and research to modeling, evaluation, deployment, monitoring and enhancement cycles.
Collaborate closely with Product, Engineering, and Business stakeholders to translate business needs into scalable AI solutions.
Ensure production-grade standards, model performance monitoring, and continuous improvement.
Stay up to date with advances in machine learning, GenAI, and LLM technologies, and translate them into business impact.
Take responsibility for hiring, onboarding, and developing top data science talent.

Lead the charge in transforming our product and preparing it for the agentic age.
Design, build, and deploy generative AI-powered features across our product.

Identify opportunities for AI integration by proactively exploring FinOps use cases and user needs

Prototype and validate new AI use cases quickly and iterate based on internal and external feedback

Collaborate cross-functionally with product, design, and backend teams to drive innovation from concept to production

Stay current with the fast-moving generative AI landscape and evaluate new models, APIs, and tools (e.g., OpenAI, Anthropic, Hugging Face, AWS Bedrock, open-source LLMs).

Live in the future and track new innovations and paradigms in this fast evolving field and identify opportunities to integrate them into the product

Implement safeguards, prompt engineering techniques, and usage monitoring to ensure high-quality AI outputs

Optimize model performance, inference time, and cost efficiency within AWS infrastructure

Do you want to take part in shaping the future of AI-driven materials discovery? We are seeking a talented and highly motivated student for a Joint PhD at Bar-Ilan University (BIU), to conduct cutting-edge research in collaboration with our teams, on next-generation Foundation Models for Materials Science.
The work will focus on developing novel multi-modal foundation models for materials discovery, leveraging large-scale data, representation learning, and uncertainty estimation. The candidate will work closely with academic and industry researchers, contributing to both methodological advances and real-world impact across scientific and industrial domains.
What youll be doing:
Conduct research in deep learning and AI for materials science, with a focus on foundation models, multi-modal learning, and representation learning.
Develop and train large-scale models for materials discovery and prediction tasks.
Collaborate with researchers and engineers across and academia.
Lead independent research under academic supervision, while contributing to team efforts.
Publish results in top-tier conferences and journals, and present findings clearly and effectively.

We are always looking for exceptional talent to join us on the journey!
We are always looking for exceptional talent to join us on the journey!


Your Mission

As an MLOps Engineer at Nuvei, your mission is to design, build, and operate the platforms that power our machine learning and generative AI products spanning real-time use cases such as large-scale fraud scoring, MCP & agentic workflows support. Youll create reliable CI/CD for models and Agents, robust data/feature pipelines, secure model serving, and comprehensive observability. You will also support our agentic AI ecosystem and Model Context Protocol (MCP) services so that models can safely use tools, data, and actions across .
You will partner closely with Data Scientists, Data/Platform Engineers, Product, and SRE to ensure every model from classic ML to LLM/RAG agents moves from prototype to production with strong reliability, governance, cost efficiency, and measurable business impact.
Responsibilities:
Operate & Develop ML/LLM platforms on Kubernetes + cloud (Azure; AWS/GCP ok) with Docker, Terraform, and other relevant tools
Manage object storage, GPUs, and autoscaling for training & low-latency model serving
Manage cloud environment, networking, service mesh, secrets, and policies to meet PCI-DSS and data-residency requirements
Build end-to-end CI/CD for models/agents/MCP tooling (versioning, tests, approvals)
Deliver real-time fraud/risk scoring & agent signals under strict latency SLOs.
Maintain MCP servers/clients: tool/resource definitions, versioning, quotas, isolation, access controls
Integrate agents with microservices, event streams, and rule engines; provide SLAs, tracing, and on-call runbooks
Measure operational metrics of ML/LLM (latency, throughput, cost, tokens, tool success, safety events)
Enforce governance: RBAC/ABAC, row-level security, encryption, PII/secrets management, audit trails.
Partner with DS on packaging (wheels/conda/containers), feature contracts, and reproducible experiments.
lead incident response and post-mortems.
Drive FinOps: right-sizing, GPU utilization, batching/caching, budget alerts.

Were looking for a seasoned and driven Team Lead to head our NLP & Speech team. In this role, youll lead a high-performing team of researchers and engineers developing state-of-the-art capabilities in real-time transcription, semantic understanding, content generation, and AI-powered editing tools. Leveraging our unique access to vast multimodal datasets and large-scale compute, your team will drive ambitious applied research projects from concept to deployment - powering intelligent, intuitive experiences for millions of content creators.

Required Staff Software Engineer, Cloud Storage, AI/ML
Tel Aviv, Israel
Responsibilities
Design and implementation of high-complexity features. Deliver production-ready code for storage solutions that address the specific demands of AI/ML workloads.
Architect scalable and performant storage solutions. Make data-driven decisions to optimize system efficiency and reliability.
Identify and resolve performance bottlenecks and intricate system issues. Develop innovative, practical solutions to technical issues that arise at the intersection of storage and ML.
Partner with product and engineering stakeholders to translate customer requirements into technical specifications, ensuring the incubations output aligns with broader Cloud goals.

The financial risk management (FRM) machine learning principal will be the most senior machine learning engineer and strategist for financial risk at Meta. The principal will enable the risk organization to deliver significant lift over current long range objectives for friction and leakage through the generation of new Machine Learning opportunities for the organization and support of successful delivery of the risk management ML architecture. This person will partner closely with the FRM engineering leader (Director level) and be part of Metas risk management leadership circle.
Software Engineer, ML (Technical Leadership) Responsibilities
Address core business and technical machine learning opportunities: elevate the existing portfolio of machine learning solutions to be state-of-the-art for minimizing Metas financial losses (due to leakage, good revenues loss and friction). Following are a few examples of technical and business problems we aim to address. - Provide a solution for optimizing the risk machine learning model ensemble (covering the entire end-to-end advertiser funnel including detection, decisioning, enforcement and remediation) through optimization of the current model portfolio and individual models. - Minimize the impact of the prolonged financial fraud feedback loop. - Improve models measurement and performance. - Optimize data/label strategy. - Optimize balance between specific targeted model strategy and broad umbrella model strategy to optimize for short and long term benefits
Lead Research and Introduction of Advanced Technologies: - Collaborate with Financial Integrity's senior ML Engineers to lead the research and introduction of deep learning and Large Language Model (LLM) technologies. - Remain current on industry-wide advancements in ML and introduce relevant advancements in Financial Risk Management
Collaborate on Next-Generation ML Architecture: - Work closely with financial harms principals and risk management tech leads to deliver the next-generation ML architecture for Meta's risk management system. - Collaborate with Principal ML engineers from across the company to adopt best industry and Meta practices within the FRM team. - Resolve or mitigate design dilemmas, balancing business and technical trade-offs. - Identify and initiate opportunities for collaboration and impact with other organizations at Meta
Identify and Initiate New Business Opportunities: - Collaborate with Meta FinTech, Central Integrity and Core Ads Growth partnerships to identify and initiate new business opportunities based on third-party capabilities. - Conduct proof of concept for different opportunities and initiate integrations to enhance business performance
Grow Other Senior ML Engineers - Actively invest in the growth of other senior ML engineers through goal-driven formal and informal mentorship. Provide regular feedback to other engineers regarding their technical work.

Are you an AI Analyst ready to translate raw model output into a reliable, high-quality production system? Do you excel at the craft of Prompt Engineering and thrive on the challenge of ensuring Generative AI output meets rigorous quality standards in mission-critical applications? Join our R&D Operations Team. You will be the AI analyst responsible for the end-to-end quality, performance, and operational tuning of our Generative AI-driven support system (Co-Pilot). Your mission is to actively shape the model's intelligence, govern the data it uses, and implement the mechanisms that guarantee its accuracy, directly accelerating our customer support engineering velocity.
Key Responsibilities
Support & Customer Advocacy: Champion the Support Journey in Engineering. Develop in-product support instructions that reflect and address real incoming cases, enhancing the quality and effectiveness of AI feature solutions.
Model Quality Validation: Use existing evaluation platforms and methodologies to validate production models. Monitor quality metrics to continuously assess and rank AI answers for accuracy and reliability.
Prompt Engineering & CT Loop: Drive the Continuous Training loop through systematic prompt engineering (refining and versioning inputs). Analyze failures to define R&D actions or features needed to close model performance gaps.
AI Knowledge Governance: Act as AI Content Governor, implementing controls to verify and ingest compliant knowledge base content, ensuring a quality data source.
Cross-Functional SME: Serve as the AI Support Co-pilot Subject Matter Expert, partnering with R&D and Support Enablement to translate quality issues into core model logic improvements and feature development.