לוח משרות דרושים NLP/Machine Learning בתל אביב יפו

We are seeking a Backend Team Lead to spearhead the development of ludeo.ai, our GenAI-powered product that enables users to generate interactive (gaming experiences) directly from prompts or video content. This is a high-impact leadership role at the intersection of backend architecture, multimodal AI, and real-time systems. You will architect and lead the AI engine that transforms unstructured inputs (text/video) into structured, interactive gaming playable moments.

What Youll Do

Lead & Mentor: Build and manage a high-performing backend/AI engineering team, drive architectural decisions, and foster rapid innovation while maintaining production-grade reliability.
Design AI-Native Systems: Architect scalable microservices powering complex AI workflows. Design and implement Retrieval-Augmented Generation (RAG) pipelines, embedding strategies, and vector database infrastructure (e.g., Pinecone, Weaviate, Milvus, PGVector). Optimize retrieval, prompt orchestration, latency, and cost.
Agentic Workflows: Design multi-agent systems using planner/executor/tool-calling patterns. Implement stateful, multi-step AI workflows with frameworks such as LangChain, CrewAI, AutoGen, or similar. Build evaluation, observability, and safety mechanisms for LLM systems.
Multimodal AI: Integrate multimodal models (vision + text) to understand video and translate it into structured form.
Scale & Infrastructure: Ensure robustness, security, and high availability on AWS/Kubernetes. Design distributed systems that handle real-time data and AI workloads efficiently.
Collaborate: Work closely with Product and Design to translate GenAI capabilities into stable, scalable production features.

We are seeking a Backend Engineer to join the development of ludeo.ai, our GenAI-powered product that enables users to generate interactive (gaming experiences) directly from prompts or video content. This is a high-impact role at the intersection of backend architecture, multimodal AI, and real-time systems. You will contribute to the AI engine that transforms unstructured inputs (text/video) into structured, interactive gaming playable moments.

What Youll Do

Design AI-Native Systems: Design and implement scalable microservices powering complex AI workflows. Design and implement Retrieval-Augmented Generation (RAG) pipelines, embedding strategies, and vector database infrastructure (e.g., Pinecone, Weaviate, Milvus, PGVector). Optimize retrieval, prompt orchestration, latency, and cost.
Agentic Workflows: Design and implement multi-agent systems using planner/executor/tool-calling patterns. Implement stateful, multi-step AI workflows with frameworks such as LangChain, CrewAI, AutoGen, or similar. Build evaluation, observability, and safety mechanisms for LLM systems.
Multimodal AI: Integrate multimodal models (vision + text) to understand video and translate it into structured form.
Scale & Infrastructure: Ensure robustness, security, and high availability on AWS/Kubernetes. Contribute to distributed systems that handle real-time data and AI workloads efficiently.
Collaborate: Work closely with Product and Design to translate GenAI capabilities into stable, scalable production features.

This role is designed for a researcher who is "hands-on." We value deep theoretical knowledge, but we require the ability to translate that theory into productive, working code within a limited timeframe.

What youll be doing
Long-Horizon Research: Lead specific, deep-dive research initiatives that require advanced methodology (e.g., novel anomaly detection architectures or LLM-based reasoning for security threats) without the pressure of daily sprint cycles.
LLM Innovation: Design and prototype advanced LLM workflows (RAG, Agents, Fine-tuning) to solve specific security challenges that standard APIs cannot handle.
Academic-to-Industry Bridge: Act as a knowledge hub for the team; bring SOTA academic concepts, recent paper findings, and novel techniques into the teams toolkit.
High-Impact Prototyping: Build functional Proofs of Concept (POCs) that the full-time engineering team can eventually operationalize.

We are seeking a hands-on Applied AI Scientist to join our core R&D team and drive the development of next-generation AI systems for autonomous driving. This role sits at the intersection of applied research and deployment - you will go from reading papers to shipping production systems. You will work directly on our multi-layered autonomy architecture, with a primary focus on real-time predictive models for driving decisions.
A deep technical role for someone who thrives on turning cutting-edge research into real, working systems under hard constraints.
Responsibilities:
Own the research-to-deployment cycle for predictive driving models - from literature review and prototyping through to production integration
Design, implement, and iterate on real-time predictive models, including vision-language models, motion prediction models, and inverse reinforcement learning approaches (e.g., imitation learning, reward recovery)
Collaborate on higher-level reasoning systems, contributing to vision-language-action models that handle complex edge cases and long-horizon planning
Bridge cloud-scale training with edge deployment - work on model compression, quantization, speculative decoding, and efficient inference for embedded automotive platforms
Evaluate and integrate state-of-the-art techniques from the broader AI research community into our autonomy stack
Collaborate closely with internal R&D teams to unblock technical challenges, accelerate delivery, and raise the overall technical bar.

we aren't building a single, generic chatbot. We are building a Composable AI Microservice Architecture, a swarm of hundreds of hyper-specialized AI services, each meticulously "programmed" to solve small, focused tasks with high precision. This fleet powers Ava, our AI support engine, and a suite of cutting-edge generative tools for travel and expense management.
As a Senior AI Ops / MLOps Engineer, you are the architect of the platform that makes this scale possible. You will move beyond traditional MLOps to manage a "factory" of Language Models. Your challenge is one of orchestration and standardization, ensuring that every service in the swarm meets a rigorous bar for quality, reliability, and cost-efficiency.
What You'll Do:
Orchestrate the AI Fleet: Build and own the runtime environment for 100+ specialized AI services. Manage model routing, context versioning, and standardized memory/history stores.
High-Density Inference Optimization: Design and implement SageMaker Multi-Model Endpoints (MME) and Inference Components to serve multiple tuned SLMs per GPU, maximizing hardware utilization while minimizing latency.
Deterministic Service Excellence: Treat reliability as a layered engineering problem. Build deterministic "shells" around probabilistic LM outputs, prioritizing data-layer validation and strict serialization.
Automated Evaluation & Observability: Implement "LLM-as-a-judge" patterns and automated benchmarking to detect semantic drift and hallucinations across the fleet before they impact the user.
Standardize the Workflow: Obsess over building reusable patterns and Terraform-based infrastructure that eliminate "snowflake" configurations, allowing us to deploy new specialized AI tasks in minutes.
Agency Strategy: Partner with AI Researchers to find the "Goldilocks zone" for agentic autonomy-balancing the flexibility of LLM tool-use with the precision required for production stability.

we are seeking an experienced data scientist to join our Model Validation team. The ideal candidate will bring a blend of data science knowledge and analytical skills, understanding in Finance and an understanding of the regulatory landscape of credit lending. As a core team member, you will work closely with model developers to assess model performance, data quality, conceptual soundness, and produce thorough, compliant documentation for regulatory review, ensuring our models meet the highest standards. You will be required to challenge the models, helping deliver high-quality validated models for loan pricing and underwriting.
Key Responsibilities:
Conduct thorough validation of ML models to assess performance, data quality, robustness, conceptual soundness, regulatory requirements and more
Conduct independent tests to assess performance and limitations, and establish model assumptions
Suggest and develop alternative models to challenge and improve existing models
Provide insights into the' strengths and weaknesses of models in terms of Data Science methodology and Finance as well as recommend practical solutions.
Prepare comprehensive validation documentation
Support the ongoing monitoring and improvement of existing predictive models
Stay updated on the latest research in ML credit risk modeling and financial news, ensuring that our practices align with industry standards

Were looking for a Senior AI/ML Architect to help shape how our customers implement AI. In this role, youll design scalable, end-to-end ML solutions, from classical models to generative AI, and guide customers from concept to production. Youll work closely with C-level leaders, data scientists, and engineers to deliver impactful, real-world AI outcomes.
📍 Work location - hybrid from Tel Aviv
If you are interested in this opportunity, please submit your CV in English.
Responsibilities
Design and deliver end-to-end ML solutions, including classical ML, deep learning, and generative AI
Create technical proposals, reference architectures, and deployment plans
Lead workshops, POCs, and architecture reviews with startup customers
Translate business needs into scalable ML systems
Implement distributed training workflows and LLM serving infrastructure
Build MLOps pipelines for tracking, versioning, and monitoring
Provide technical guidance to customers and internal teams
Stay up to date on ML trends and contribute to internal enablement
Frequent travels - locally (on-demand to meet with customers and partners and attend local events) and abroad (at least once a quarter).

Technical teams are overwhelmed by manual, repetitive tasks, fragmented tools, and rapidly growing data. These challenges increase errors, slow decision-making, and prevent teams from focusing on strategic initiatives.
What You'll Do:
Build and deploy clusters of AI agents that tackle multiple use cases across the IT domain.
Work hands-on with LLMs-installing, running, and fine-tuning models. We are not just calling APIs.
Build backend services for data ingestion, AI pipelines, and integrations.
Integrate open-source AI modules directly: read, tweak, and ship.
Operate in AWS (Lambdas, APIs, infrastructure) to power AI-driven features.
Collaborate directly with customers-strong English communication is essential.

If youre looking to build something big, solve real problems, and grow fast-wed love to meet you.

You will own AI infrastructure and agentic interaction layer on top of our Data Platform.

The data layer is the product.
The AI layer is the multiplier.
The interaction model between them is the future.

Your responsibility is to design and build:

The AI infrastructure that enables safe, scalable, production-grade AI features

The ability to interact with Data Layer through LLMs and agentic workflows

Guardrails, governance, and system boundaries for AI-driven data access

The foundations that allow AI to be powerful without compromising correctness or trust

We are looking for a Data Operations Team Lead to lead and scale our data operations function. This role sits at the intersection of data, algorithms, and operations, and is critical to the success of our computer vision and AI models.
You will lead a distributed team responsible for data collection, annotation, quality control, and delivery, while working closely with Algorithm teams to understand model needs, prioritize requests, and ensure reliable, high-quality data pipelines.
This is a hands-on leadership role - you will manage people and processes, but also actively design, build, and improve data workflows.
About The Role
Lead and manage the Data Operations team, including annotation teams in Israel and a large remote team in India
Serve as the primary interface between Data Operations and Algorithm teams: understand model requirements, prioritize tasks, and plan data delivery
Own end-to-end data workflows, from model improvement needs through data definition, annotation guidelines, execution, and delivery
Ensure high data quality through close monitoring, validation, troubleshooting, and root-cause analysis
Design and maintain clear annotation guidelines, documentation, and training materials
Closely manage remote annotation teams, including weekly syncs, hands-on oversight, and deadline management
Own and operate the annotation pipeline using industry tools (e.g., CVAT)
Monitor progress, track performance, and continuously improve efficiency and quality
Own annotation budgets, monthly reporting, and validation of hours and outputs
Evaluate, benchmark, and implement new tools and processes in the data and annotation domain
Work hands-on with scripts, AI tools, and monitoring systems as needed to support data quality and operations.

It starts with you - ML Engineering Team Lead responsible for leading a team delivering end-to-end ML systems. This role combines people management with ownership of model training, evaluation, deployment, and ML platform standards
If you want to grow your skills building AI products for mission-critical AI, join our companys mission - this role is for you.
The Responsibilities
Lead and mentor ML Engineers delivering production ML systems.
Own technical direction for model architectures, training pipelines, and evals.
Review designs and code; ensure scalability, reliability, and data quality.
Define standards for experimentation, reproducibility, and monitoring.
Partner with product and platform teams on roadmap and prioritization.
Balance hands-on technical contribution with people management.
Build a culture of measurement, rigor, and continuous improvement.

It starts with you - a senior ML engineer responsible for building, training, evaluating, and operating machine learning systems in production. The role focuses on data pipelines, model training, experimentation, evaluation, and scalable deployment.
If you want to grow your skills building AI products for mission-critical AI, join our companys mission - this role is for you.
The Responsibilities
Design, train, and evaluate ML models for production use.
Build and maintain data pipelines for training, validation, and inference.
Own experimentation workflows: feature engineering, training runs, and comparison.
Implement model evals, monitoring, and drift detection.
Package and deploy models to production systems.
Optimize training and inference performance, cost, and reliability.
Collaborate with data, platform, and product teams.
Mentor engineers and promote ML engineering best practices.

At our company, we redefine cyber defense vision by combining AI and human expertise to create products that protect nations and critical infrastructure. This is more than a job; its a Dream job. we are where we tackle real-world challenges, redefine AI and security, and make the digital world safer. Lets build something extraordinary together.
our company'sAI cybersecurity platform applies a new, out-of-the-ordinary, multi-layered approach, covering endless and evolving security challenges across the entire infrastructure of the most critical and sensitive networks. Central to our company's proprietary Cyber Language Models are innovative technologies that provide contextual intelligence for the future of cybersecurity.
At our company, our talented team, driven by passion, expertise, and innovative minds, inspires us daily. We are not just dreamers, we are dream-makers.
The Responsibilities
Conduct cutting-edge research in natural language processing and large language models.
Design, train, and optimize large-scale neural network models for advanced applications.
Transition research projects from ideation through deployment and scaling.
Collaborate closely with cross-functional teams, including domain experts, product managers, and engineers, to deliver impactful AI solutions.
Define and contribute to the AI and NLP product roadmap.

We are looking for a Senior Machine Learning Algorithm Engineer who is passionate about solving complex, real-world problems at the intersection of hardware and software.
Location: Haifa \ Tel-Aviv in hybrid model.
Responsibilities
Develop advanced ML algorithms using data from our company Agents embedded in silicon. Example domains include:
Lifetime predictions
Clustering and dimensionality reduction
Parametric modeling and screening
Time-series forecasting
Lead end-to-end algorithm lifecycle: ideation → research → POC → design → implementation → validation → deployment.
Collaborate closely with cross-functional teams and directly with customers to continuously refine and enhance deployed solutions.
Drive integration of AI-enhanced tools and intelligent pipelines into ML workflows.

Required Senior Machine Learning Engineer I - GenAI Applications
20031
Leadership/Team Quote:
This opening is for the GenAI Infra team in the Marketplace AI department.
The GenAI Infra team builds the Agents platform which is used for all agnetic and non-agentic flows. This team is responsible for both the GenAI agents and the orchestration around them, helping support applications such as the AI Trip Planner, Free text search, etc.
Role Description:
As Senior Machine Learning Engineer, youll work with top notch engineers and data scientists from the team on bringing it to the next level and enabling optimal user experience. The work will focus on building, deploying and serving GenAI capabilities (Agents, Tools and the orchestration between them) using the most advanced technologies and models.
Key Job Responsibilities and Duties:
Deploying machine learning models: Design, develop and deploy in collaboration with scientists, scalable machine learning models and algorithms that provide content related insights and generative AI applications, ensuring scalability, efficiency, and accuracy.
Evaluating possible architecture solutions by taking into account cost, business requirements, emerging technologies, and technology requirements, like latency, throughput, and scale.
Generative AI Development: Contribute to the development of generative models such as GPT (Generative Pre-trained Transformer) variants or similar architectures for creative content generation, Q&A, translation or other innovative applications.
Deployment and integration: Work closely with software engineers to integrate machine learning models into production systems. Ensure seamless deployment and efficient model inference in real-time environments. Collaborate with DevOps to implement effective monitoring and maintenance strategies.
Owning a service end to end by actively monitoring application health and performance, setting and monitoring relevant metrics and acting accordingly when violated.
Maintain clean, scalable code, ensuring reproducibility and easy integration of models into production environments, including CI/CD.
Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions.