לוח משרות דרושים NLP/Machine Learning

Were hiring an AI Engineer for our AI Core team to build and scale AI across products and workflows, from agentic document understanding to multi-agent systems and ML solutions. This is a hands-on, cross-functional role where youll prototype quickly, build robust evaluations, and ship production-grade AI with measurable business impact.
What youll do:
Build and ship AI/ML solutions using LLMs, agents, retrieval patterns (RAG), and document understanding models, alongside classic ML (classification, ranking, time-series).
Prototype and iterate quickly: turn ideas into high-quality POCs, validate feasibility, and evolve them toward production.
Shape technical direction and evaluate models and approaches using clear benchmarks, metrics, and real-world impact measurement.
Research and apply emerging techniques, e.g., multimodal/document AI, agentic frameworks, synthetic data generation, and architectural approaches.
Partner closely with product, engineering, and domain experts to identify opportunities and deliver end-to-end solutions.
Help shape scalable, secure architecture and best practices for AI delivery.
Location: Herzliya | Hybrid work model

We are looking for a Senior Data & Machine Learning Engineer to operate at the intersection of data platform engineering and machine learning enablement. This role is responsible for building scalable, efficient, and reliable data systems while enabling Data Science and Analytics teams to develop and deploy ML-driven features.

You will take ownership of the data and ML infrastructure layer, ensuring that pipelines, storage models, and compute usage are optimized, while also shaping how data workflows and ML solutions are designed across the organization.


Responsibilities
Data Platform & Infrastructure

Design, build, and maintain scalable data pipelines and storage systems supporting analytics and ML use cases
Ensure compute and cost efficiency across pipelines, storage models, and processing workflows
Own and improve data orchestration, transformation, and serving layers (e.g., Spark, DBT, streaming/batch systems)
Build and maintain shared infrastructure components, including:
IO managers and data access abstractions
Integrations with DBT, Spark, and other data frameworks
Internal tooling to improve developer productivity and reliability
ML Enablement & Collaboration

Partner closely with Data Science to design and productions ML solutions for new features and research initiatives
Translate experimental models into robust, scalable production systems
Support feature engineering, training pipelines, and inference workflows
Help define best practices for ML lifecycle management (training, validation, deployment, monitoring)
Data Quality, Governance & Best Practices

Enforce best practices for building and maintaining data processes across Data Analyst and Data Science teams
Define standards for:
Data modeling and transformations
Pipeline reliability and observability
Testing, versioning, and documentation
Improve data quality, consistency, and discoverability across the organization
Performance & Reliability

Optimize systems for performance, scalability, and cost efficiency
Monitor and troubleshoot data pipelines and ML systems in production
Implement observability (logging, metrics, alerting) across data workflows

Required Senior ML Engineer - Applied AI Engineering Group
The Dream Job
It starts with you - an engineer driven to build the ML platform that turns research into reliable, production-grade intelligence. You care about reproducibility, low-friction experimentation, and infrastructure that earns the trust of the scientists and researchers who depend on it daily. You'll architect and ship our ML platform - training pipelines, model serving, feature stores, experiment tracking, and compute orchestration - turning models into production capabilities 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.
If you want to make a meaningful impact, join our mission and build the ML platform that drives Sovereign AI products - this role is for you.
The Dream-Maker Responsibilities
Build and operate ML training infrastructure - distributed training pipelines, compute scheduling, and reproducible experiment workflows that data scientists rely on daily.
Own model serving and inference systems - packaging, deployment, autoscaling, A/B testing, canary rollouts, and latency/cost optimization for production models.
Run feature stores, model registries, and dataset versioning - enabling self-serve feature engineering, model lineage, and reproducible experiments across teams.
Build experiment tracking and evaluation infrastructure - automated evals, comparison dashboards, drift detection, and monitoring that give teams visibility into model behavior and performance.
Build and maintain production pipelines for training, fine-tuning workflows, and serving domain models - owning reliability, reproducibility, and scale.
Build and maintain the monitoring and observability layer - model performance tracking, data and prediction drift detection, data quality validation, and alerting.
Improve performance and cost across the ML stack - training throughput, inference latency, batch vs. real-time tradeoffs, and compute cost management.
Ship shared tooling - libraries, templates, CI/CD for models, IaC, and runbooks - while collaborating across Data Platform, AI, Data Science, Engineering, and DevOps. Own architecture, documentation, and operations end-to-end.

Required ML Engineering Team Lead - Applied AI Engineering Group
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.
The Dream-Maker 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.

Required Senior NLP Researcher
Tel Aviv Full-time
About The Position
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. This is where we tackle real-world challenges, redefine AI and security, and make the digital world safer. Lets build something extraordinary together.
Our AI 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. Built as part of a broader sovereign AI platform, our technology is designed to operate in on-premise, private cloud, and air-gapped environments, enabling nations to maintain full control over their data, infrastructure, and AI capabilities. Central to our proprietary Cyber Language Models are innovative technologies that provide contextual intelligence for the future of cybersecurity.
Our talented team, driven by passion, expertise, and innovative minds, inspires us daily. We are not just dreamers, we are dream-makers.
The Dream-Maker 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.

Realize your potential by joining the leading performance-driven advertising company!
As a Team Leader on the advertiser side ML group in our TLV Office, youll play a vital role in shaping how user intent is modeled at scale and how rich behavioral data is transformed into smarter recommendations. You will lead the design and evolution of large-scale Deep Learning models that directly improve advertiser performance and marketplace efficiency. By guiding team members and driving technical direction, youll turn modeling gaps into scalable production solutions. Your work will influence how millions of users discover content and how advertisers achieve measurable impact.
How youll make an impact:
Lead the design and evolution of our companys models
Own end-to-end ML delivery in production
Lead and develop ML engineers
Translate marketplace objectives into modeling strategy
Play a part of our companys tech organizations growth in the agentic development era.

Realize your potential by joining the leading performance-driven advertising company!
As a Senior Machine Learning Engineer, youll play a vital role in turning algorithm prototypes into shippable products that will have a significant and immediate impact on the companys revenue
How youll make an impact:
As a Senior Machine Learning Engineer, youll bring value by:
Be responsible for the entire algorithmic lifecycle in the company: data analytics, prototyping of new ideas, implementing algorithms models in a production environment and then monitoring and maintaining them
Turn algorithm prototypes into shippable products that will have a significant and immediate impact on the companys revenue
Work on a daily basis with some of the hottest trends in todays job market: machine/deep learning, big data analytics/engineering and cloud computing
Apply your scientific knowledge and creativity to analyze large volumes of diverse data and develop algorithmic solutions and models to solve complex problems
Influence directly on the way billions of people discover the internet
Work on projects such as Internet Personalization, Content Feed, Real Time Bidding, Video Recommendations and much more.

Realize your potential by joining the leading performance-driven advertising company!
As a Senior MLOps Engineer on the Infra group, youll play a vital role in develop, enhance and maintain highly scalable Machine-Learning infrastructures and tools.
How youll make an impact:
As a Senior MLOps Engineer Engineer, youll bring value by:
Develop, enhance and maintain highly scalable Machine-Learning infrastructures and tools, including CI/CD, monitoring and alerting and more
Have end to end ownership: Design, develop, deploy, measure and maintain our machine learning platform, ensuring high availability, high scalability and efficient resource utilization
Identify and evaluate new technologies to improve performance, maintainability, and reliability of our machine learning systems
Work in tandem with the engineering-focused and algorithm-focused teams in order to improve our platform and optimize performance
Optimize machine learning systems to scale and utilize modern compute environments (e.g. distributed clusters, CPU and GPU) and continuously seek potential optimization opportunities.
Build and maintain tools for automation, deployment, monitoring, and operations.
Troubleshoot issues in our development, production and test environments
Influence directly on the way billions of people discover the internet.

Realize your potential by joining the leading performance-driven advertising company!
As a Staff MLOps Engineer on the Infra group, youll play a vital role in develop, enhance and maintain highly scalable Machine-Learning infrastructures and tools.
How youll make an impact:
As a Staff MLOps Engineer Engineer, youll bring value by:
Develop, enhance and maintain highly scalable Machine-Learning infrastructures and tools, including CI/CD, monitoring and alerting and more
Have end to end ownership: Design, develop, deploy, measure and maintain our machine learning platform, ensuring high availability, high scalability and efficient resource utilization
Identify and evaluate new technologies to improve performance, maintainability, and reliability of our machine learning systems
Work in tandem with the engineering-focused and algorithm-focused teams in order to improve our platform and optimize performance
Optimize machine learning systems to scale and utilize modern compute environments (e.g. distributed clusters, CPU and GPU) and continuously seek potential optimization opportunities.
Build and maintain tools for automation, deployment, monitoring, and operations.
Troubleshoot issues in our development, production and test environments
Influence directly on the way billions of people discover the internet.

Required ML Engineering Team Lead
Why us:
Youll work in an awesome environment alongside some of the most innovative people in the industry, using cutting-edge technologies and tools (video editing, Gen AI, data, etc.). You have the opportunity to directly influence the products and tools used by our clients, including sports giants such as the NBA, Bundesliga, LaLiga, ESPN - and thats just the beginning of what we have to offer! Join us and be a part of the best team in tech as we Fuel the Fandom worldwide.
As a ML Engineering Team Lead, you will lead a multidisciplinary team of algorithm and software engineers responsible for designing, building, and operating the next-generation content creation engine. The team focuses on leveraging agentic systems and advanced ML technologies to orchestrate complex, end-to-end production workflows at scale.
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 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 looking for a motivated and experienced Machine Learning Platform Engineer to join our dynamic team.

In this role, you will collaborate closely with data scientists and DevOps professionals to design and build the infrastructure, ecosystem libraries, and pipelines that power our data science initiatives. You will take ownership of model development, monitoring, and maintenance, working hand-in-hand with data scientists on a daily basis.

If youre passionate about AI, machine learning, and writing high-quality code-and are eager to contribute to innovative, impactful do good projects in the digital health space-wed love to hear from you!

What you'll be doing:
Design, develop, and maintain our machine learning ecosystem libraries.
Build and manage data science code, Docker images, and Kubeflow Pipelines (KFP).
Create and maintain CI scripts to ensure seamless integration and delivery.
Conduct thorough code reviews to uphold high-quality standards.
Collaborate closely with data scientists, understanding and addressing their evolving needs.
Work alongside software developers to seamlessly integrate machine learning models into production systems.
Stay current with the latest advancements in machine learning, leveraging innovative techniques to enhance the companys products and services.

Design and execute research projects using advanced methodologies and tools to generate actionable insights that improve decision-making.
Analyze data and apply critical thinking to identify innovation opportunities and optimize research processes.
Prepare scientific reports and presentations to effectively communicate findings and recommendations to stakeholders.
Collaborate across teams and consult with experts, demonstrating adaptability, receptiveness to feedback.

We are looking for an experienced Deep Learning & AI Infrastructure Engineer to join our AI team. In this role, you will develop and deploy advanced deep learning models for signal processing and build the infrastructure and tooling required to scale AI training and inference in both research and production environments.
You will be responsible for the end-to-end lifecycle of AI workloads, including data ingestion and preprocessing pipelines, model training infrastructure, efficient inference systems, and automated experiment tracking.
Key Responsibilities
Design, implement, and optimize deep learning models and pipelines for signal and image analysis.
Build and maintain AI infrastructure components to support model training, versioning, monitoring, and deployment.
Develop scalable data processing pipelines for datasets, including ingestion, labeling, augmentation, and preprocessing at scale.
Implement automated training workflows using tools such as ClearML or other experiment tracking platforms, ensuring reproducibility and model governance.
Collaborate with system engineers to integrate AI models into real-time radar systems and ensure efficient inference performance.
Optimize distributed training and resource utilization.
Run performance tuning, testing, and benchmarking for both training and inference workloads.
Document infrastructure components, training procedures, and model lifecycle processes.

we are looking for highly skilled Ph.D. level researchers for an Algorithmic Trading Research Group in Tel Aviv. our company office is located at the TLV expo center, right next to TLV University train station and offers highly competitive compensation packages.
Our formula for success is to hire exceptional people, encourage their ideas and reward their results with competitive compensation packages.
Researchers at our comoany apply tools from a variety of disciplines including statistics, control theory, machine learning, optimization, and signal processing to develop algorithmic trading strategies. Our research team collaborates on idea generation and strategy development, while encouraging independent exploration and original approaches. Researchers have access to clean data integrated with a high performance computing grid and the support of dedicated software developers.
Job Description
Develop algorithmic trading strategies and improve existing strategies
Identify inefficiencies in the financial markets and characterize them with mathematical models
Predict price movements using statistics, signal processing and machine learning
Apply optimization techniques to construct optimal portfolios
Design automated trading agents that achieve efficient execution
Direct software developers in the translation of the research into production software.