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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.

In this role, you will translate high-level product and business goals into robust, production-ready solutions, working hands-on across the full lifecycle - from research and system design to deployment, monitoring, and continuous improvement. You will collaborate closely with Product, Platform, Data, and other R&D teams in a fast-paced, agile environment, ensuring that algorithms and software are seamlessly integrated into real-world, high-throughput systems.
You will combine technical leadership, architectural thinking, and people leadership to drive innovation, execution, and delivery, while shaping how intelligent, automated content is created and scaled across our companys products.
What youll do:
Lead and mentor a multidisciplinary team of algorithm and software engineers, fostering ownership, technical excellence, and professional growth.
Own the delivery of complex, end-to-end systems for automated content creation, from early design and experimentation through production deployment, monitoring, and iteration.
Drive the design and orchestration of agentic systems, working hands-on with the team to integrate ML models, business logic, and software infrastructure across research, engineering, and production at scale.
Translate product and business challenges into technical solutions, defining system architecture, prioritizing initiatives, and guiding execution in an agile environment.
Ensure production readiness and reliability, overseeing deployment, monitoring, performance, and continuous improvement of algorithms and services in a live environment.
Collaborate closely with Product, Platform, Data, and other R&D teams, aligning technical execution with product strategy and business goals in a multidisciplinary setting.
Continuously evolve technically and professionally, encouraging knowledge sharing, joint problem-solving, and awareness of emerging trends in ML, agentic systems, and large-scale software architectures.

we are a modern, full-stack observability platform transforming how businesses process and understand their data. Our unique architecture powers in-stream analytics without reliance on expensive indexing or hot storage. We specialize in comprehensive monitoring of logs, metrics, trace and security events with features such as APM, RUM, SIEM, Kubernetes monitoring and more, all enhancing operational efficiency and reducing observability spend by up to 70%.
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 youre 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.

Build and ship end-to-end AI-driven features and agentic workflows- from experimentation through validation to production deployment (Python and C#).
Design scalable backend services that support production-grade agentic AI capabilities.
Develop and improve solutions powered by LLMs, AI agents, multi-agent systems, and generative AI frameworks.
Evaluate agentic architectures and model performance, owning monitoring, reliability, and continuous improvement in production.
Collaborate cross-functionally with software engineers, data scientists, and analysts to drive AI innovation across the platform.

Build and ship end-to-end AI-driven features and agentic workflows- from experimentation through validation to production deployment (Python and C#).
Design scalable backend services that support production-grade agentic AI capabilities.
Develop and improve solutions powered by LLMs, AI agents, multi-agent systems, and generative AI frameworks.
Evaluate agentic architectures and model performance, owning monitoring, reliability, and continuous improvement in production.
Collaborate cross-functionally with software engineers, data scientists, and analysts to drive AI innovation across the platform.

Required Senior ML Applied Research Scientist
Tel Aviv
As a Senior ML Applied Research Scientist, you'll help shape the future of AI by developing and deploying cutting-edge technologies that power our products and drive our AI strategy forward.
Working alongside researchers and engineers across Speech, NLP, Applied AI, and Infrastructure, you'll tackle complex real-world challenges using large-scale production data. This role combines hands-on research, strong engineering ownership, and the opportunity to work across multiple AI domains, including Speech, NLP, LLMs, and multimodal AI.
You'll own:
Building and improving our AI capabilities across speech recognition, speaker diarization, language identification, conversational intelligence, and LLM-powered experiences.
Evaluating, benchmarking, and integrating emerging AI technologies, models, and architectures into production-ready solutions.
Driving end-to-end ownership of AI initiatives, from research and experimentation through deployment, optimization, and continuous improvement.
You'll solve:
Complex AI challenges involving real-time transcription, conversational intelligence, multimodal understanding, and large-scale production systems.
Model performance, scalability, latency, and reliability challenges by applying advanced techniques such as fine-tuning, distillation, prompting, inference optimization, and data-centric improvements.
You'll impact:
The advancement of our AI platform and future product capabilities, enabling customers to gain deeper insights and make better business decisions.
The adoption of innovative AI technologies that improve product quality, accelerate development, and create measurable business value at scale.

Are you a senior AI researcher who thrives at the intersection of cutting-edge computer vision and real-world impact? Do you combine deep research expertise with the engineering instincts to ship models that work in production? Join the Content team within our AI group, where youll lead applied research initiatives that transform how sports content is created and experienced.

This is an applied research role with a strong ownership mandate - youll drive the full research lifecycle from problem framing and experimental design through to production integration.

We are dedicated to revolutionizing sports production. Our AI-powered solutions automatically capture sports events and transform them into personalized content, turning key moments into engaging experiences for players, teams, and fans. As a Senior AI Research Scientist, you will have direct impact on the companys innovation roadmap, leading core AI capabilities that power our platform at scale.

As part of your role, you will:
Own the research lifecycle end-to-end: problem definition, literature review, experimental design, model development, evaluation, and production integration.
Design, implement, and evaluate state-of-the-art deep learning models for computer vision tasks including detection, tracking, event recognition, and temporal modeling in video.
Drive applied research projects with clear product impact - not just experimentation, but shipping models that run reliably in production.
Collaborate closely with ML engineers, infrastructure engineers, and QA to ensure smooth model integration and performance in real-world conditions.
Contribute to our research culture: present findings internally, propose new research directions, and stay at the forefront of computer vision and deep learning.
Influence architectural decisions around model design, data pipelines, and evaluation frameworks.

Required Senior AI Infrastructure Engineer
As a Senior AI Infrastructure Engineer, you'll lead the development and scaling of our machine learning training and AI infrastructure, enabling our AI and Research teams to build, train, and deploy cutting-edge AI solutions at scale. You'll collaborate across research, engineering, and business teams to create the foundations that power our AI innovation.
Youll Own:
Developing and evolving our machine learning platform, including shared libraries, development standards, CI/CD pipelines, and ML/AI tooling.
Designing and building scalable infrastructure that supports large-scale model training, experimentation with classic models and LLM, and validation.
Driving MLOps strategy and best practices, partnering with cross-functional teams to improve developer productivity, platform reliability, and operational efficiency.
Youll Solve:
Scaling machine learning infrastructure to support growing AI workloads, increasingly complex models, and rapid experimentation cycles.
Creating standardized, efficient workflows that enable research and engineering teams to move from experimentation to production faster and more reliably.
Youll Impact:
Accelerate AI innovation by building robust infrastructure that enables teams to develop, train, evaluate and validate models efficiently.
Improve the scalability, reliability, and consistency of machine learning development across our AI organization.

we are looking for a Senior ML Engineer.
Responsibilities:
Work on core data and machine learning infrastructure, which is at the heart of offering. You will create innovative solutions for data ingestion and normalization from multiple data sources, feature engineering and feature selection, as well as actual model training and evaluation. All in large scale and completely automated.

Join GenAI team to build and extend the AI assistant embedded across our product suite. The platform is a production LLM agent system that's integrated into multiple host applications, backed by a RAG knowledge base and an evaluation-driven development workflow.

You'll work end-to-end: agent design, tool implementation, retrieval quality, integration into existing product UIs, cloud infrastructure, and evaluation/observability. The platform already ships to customers - you'll extend it, raise its quality bar, and help define where it goes next.

What you'll do

Design and evolve agents - build LLM agents with tool use, routing, and human-in-the-loop flows.
Implement tools and integrations - expose product capabilities to the agent, with multi-tenant context, via internal APIs and MCP servers.
Own retrieval quality - contribute to our RAG pipeline end-to-end: ingestion, embeddings, vector search, and reranking.
Define and evolve host integration contracts - collaborate with host application teams to integrate the assistant into product UIs built on different frontend stacks. You own the shared remote module and the integration API; host teams own their stacks.
Drive evaluation-led development - write evaluators (rule-based, LLM-as-judge, multi-turn), maintain CI eval gates, and use traces and feedback to debug production behavior.
Operate the platform - own deployments, observability, and the performance and cost of LLM-backed services.
Establish engineering practices** for AI-specific work: prompt versioning, eval coverage, testing, and code review.

Required Senior AI-Native Software Engineer
Role summary:
You will join a high-impact AI engineering team within R&D, owning problems end-to-end from understanding the business need, through architecture and implementation, to production monitoring. This is a new way of working: you'll work directly with business stakeholders, ship AI-driven capabilities at speed, and help define the methodology as we build it.
As a member of the AI Foundations organization, you will lead best practices, champion early adoption of new technologies, and influence the direction of our R&D guild - building and shipping cutting-edge agentic applications that deliver seamless experiences to our users.
Location:
Hybrid - Herzliya, Israel
Full-time
What you'll do:
Own the full development loop understand the business problem, define the solution, architect it, build it, ship it, and monitor it in production
Use AI as your primary development tool, achieving in a day what used to take a team a week
Collaborate with your team and business stakeholders to define decision logic, risk thresholds, and success metrics
Design and build evaluation frameworks as part of every solution you don't ship what you can't measure
Own production readiness monitoring, alerting, and observability go in on day one
Contribute to shaping team practices, tooling, and engineering standards across the pod
Decompose business problems into agentic workflows, orchestrated flows, and reusable capabilities.

We're building AI to tackle one of the hardest operational challenges in business: getting workforce management right across many markets, each with its own tax rules, labor laws, payment rails, and constantly shifting policies.
In this role on our AI team, you'll focus on agentic workflow automation. You'll design, build, and deploy reliable AI agents that automate complex HR, payroll, and payment workflows, handling decision-making, document intelligence, and complex tasks that typically require human judgment.
What You'll Do:
Design and build agent-based systems that automate document processing, business insights, and complex enterprise workflows
Build observability, evaluation, and feedback loops for agent behavior to improve reliability, accuracy, and trust in production
Own the technical architecture and engineering standards for agentic systems
Build or manage data pipelines to process large volumes of documents and unstructured data
Collaborate with domain experts to identify high-value automation targets and deliver end-to-end solutions
Stay current on agent frameworks, LLM capabilities and limitations, and apply emerging patterns pragmatically in production

We're building AI to tackle one of the hardest operational challenges in business: getting workforce management right across many markets, each with its own tax rules, labor laws, payment rails, and constantly shifting policies.
In this role on our AI team, you'll build and scale core AI systems that power products. You'll work across agentic document intelligence, autonomous agents, compliance AI, and ML-powered insights, prototyping quickly, building robust evaluations, and shipping production-grade AI with real business impact.
What You'll Do:
Build and ship AI/ML solutions using LLMs, agents, RAG, and document understanding models, alongside classic ML
Prototype quickly, validate feasibility, and turn strong POCs into production systems
Evaluate models and architectures, apply testing and guardrails to improve agent and service reliability
Research and apply emerging techniques: multimodal/document AI, agentic frameworks, synthetic data generation, and new architectural approaches
Work cross-functionally with product, R&D, and compliance teams to deliver end-to-end solutions
Contribute to scalable, secure architecture and engineering best practices for AI delivery

Required Applied AI Researcher- Sovereign AI Research
The Dream Job
Nations are waking up to a hard truth: critical intelligence infrastructure built on hyperscaler black boxes isn't a solution it's a dependency. The Sovereign AI Research Group exists to answer that differently.
We're not fine-tuning what already exists. We're rethinking the models architecture from the ground up modular, composable, and built with compute governance as a first-class design constraint, not an afterthought.
We operate under real-world constraints. The interesting problems live at the intersections of disciplines. That's where we operate.
This is a hands-on research role embedded within a team of senior researchers. Day to day includes training models, running benchmarks, synthesize data. The researchers you'll work alongside will challenge you technically and invest in your growth.
Responsibilities:
Open Research Tracks
Familiarity with at least one is expected:
Computer Vision: object detection, segmentation, multimodal grounding, vision-language models, contrastive and self-supervised representation learning, low-resource and few-shot visual recognition.
NLP / Speech: LLMs, NERs, relation extraction, span-based and generative IE, semantic textual similarity, multilingual and cross-lingual transfer.
Reinforcement Learning: MDPs, POMDPs, model-based and model-free RL, Online Offline methods, reward modeling, sim-to-real transfer, compute-aware planning.
Graph Learning: GNNs, graph clustering, community structure, generative methods, knowledge graph embeddings, dense and sparse semantic retrieval.
Optimization: convex and nonconvex optimization, constrained and Lagrangian methods, combinatorial and integer programming, knowledge distillation (response, feature, and relation-based), test-time optimization, Bayesian optimization, resource-aware inference.
Representation Learning: contrastive learning, self-supervised and unsupervised pre-training, disentangled representations, metric learning and embedding spaces, cross-modal and multimodal alignment, meta learning (hypernetworks), transfer learning and domain adaptation, probing and interpretability of learned representations, world models.
Neurosymbolic AI: neuro-symbolic integration, differentiable theorem proving, inductive logic programming (ILP), probabilistic soft logic (PSL), causal inference and structural causal models (SCMs), programmatic and compositional reasoning
Responsibilities:
Train and evaluate models across research tracks, iterating fast while documenting rigorously.
Build and maintain benchmarking pipelines and evaluation suites.
Curate, structure, and preprocess datasets; contribute to synthetic data generation workflows.
Run ablations and controlled experiments to support research hypotheses.
Reproduce and stress-test results from recent literature relevant to the group's work.
Collaborate across tracks and with engineering teams through to production handoff.

Required ML Platform Engineering Team Lead - Sovereign AI Engineering
We're building AI that nations own and control, deployed where almost no one else can operate. ingesting and structuring complex data, and driving practical actions that can literally impact the lives of billions of people around the world. This role helps make that real.
The Dream Job
It starts with you - a technical leader driven to build both the ML platform and the engineering team behind it. You care about reliable infrastructure, great developer experience, and growing engineers through real ownership. You'll set the technical direction for our ML platform - training pipelines, model serving, feature stores, experiment tracking, and compute orchestration - shaping how models reach production across cloud and on-prem, including air-gapped deployments. A significant part of the platform supports large language models, with unique challenges across training, evaluation, and inference in mission-critical environments. You stay close enough to the codebase to debug production issues, unblock your engineers, and make sound architecture calls.
If you want to make a meaningful impact, join our mission and lead the team that builds the ML platform driving Sovereign AI products - this role is for you.
Responsibilities
Set technical direction for the ML platform - training pipelines, model serving, feature stores, experiment tracking, and compute orchestration - through RFCs, prototypes, design reviews, and build-vs-buy decisions
Lead and grow a team of ML Engineers - hire, mentor, pair on hard problems, and raise the bar through code and design reviews
Contribute to critical systems, debug production issues, and maintain deep context on the codebase to inform technical decisions
Own operational excellence for model serving - set and enforce SLAs, run capacity planning, and keep compute costs predictable
Establish ML engineering standards - reproducible experiments, automated evals, model packaging, CI/CD for models, and observability
Support the full lifecycle of our models - from training on domain-specific data to low-latency inference powering production systems
Work closely with Data Platform, AI, Data Science, and Product teams - translate business priorities into engineering work and manage cross-team dependencies
Measure and improve developer experience - deploy friction, onboarding time, CI turnaround - as seriously as model performance.