לוח משרות דרושים NLP/Machine Learning

As a ML Engineering Team Lead at our company, 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 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 looking for a Senior NLP Algorithm Developer to join us in research and development of our next-generation automatic sports video content engine. If you are passionate about state-of-the-art NLP and Generative AI research - we are looking for you!
What youll do:
The teams main focus: NLP & Generative AI.
Research and develop end-to-end real-time solutions for sports content generation automation using state of the art deep learning algorithms for text, speech, vision, audio and data analysis.
Full R&D cycle, from problem definition and research to implementation and productization with a high level of autonomy.
In addition, part of the role & responsibilities will be:
Design, develop and implement state-of-the-art Generative models, prompt engineering and classical ML/NLP techniques to create high-quality solutions for demanding product-oriented NLP tasks.
Engage in collaboration with product, account, and customer success teams to establish success criteria and convert them into measurable objectives and quantitative measures.
Conduct training and evaluation of models within practical production scenarios.

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.

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.

Required Machine Learning Scientist II - GenAI models and applications
21539
Role Description
Leadership/Team Quote:
The Content Intelligence team builds the platform that processes millions of images and texts daily, enriching our catalog with advanced machine learning models for content understanding, classification, and personalization. These capabilities power smarter recommendations, improved search experiences, and intelligent chatbots and agents that enhance customer interactions. The team also plays a key role in the AI Trip Planner, a new Generative AI-driven project transforming how travelers plan their next vacation
Role Description:
As a machine learning scientist, your work will focus on building, training and deploying content models (Computer vision, NLP and Generative AI) using the most advanced technologies and models.
You will be responsible for identifying and proposing the most appropriate data sources and modeling techniques to solve complex problems and drive business value.
Key Job Responsibilities and Duties:
Explore and apply state-of-the-art techniques in multimodal machine learning.
Train innovative ML models (NLP, CV, LLM-finetuning), build algorithms, and engineering approaches to drive business impact..
Coding skills: ensure implementation of reusable frameworks (clean and scalable code).
Conduct data analysis with detailed metrics to evaluate models performance, labels quality, features exploration.
Work closely with machine learning engineers to ensure the model's latency/throughput meets product requirements and ensure deployment of your model to production.
Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions.

Required Machine Learning Engineer II - GenAI Applications
26947
About the team:
This opening is for the GenAI Applications Team within the Data & AI Marketplace department.
The GenAI Applications team is responsible for designing and delivering agentic, ML-powered solutions for some of our most impactful products, including booking search experiences, trip planning, and trip helpfulness. The team builds AI-driven applications and conversational agents, such as chatbots and intelligent assistants, that significantly enhance the end-to-end customer experience.
Role Description:
As a Machine Learning Engineer, you will work closely with experienced engineers and ML scientists to build scalable, production-grade GenAI applications. Your work will focus on designing, training, and deploying ML systems leveraging LLMs,, recommendation systems, and agent-based architectures, using state-of-the-art technologies. These solutions will directly power customer-facing experiences and play a key role in shaping the future of AI-driven travel products.
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, chatbots, 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.

Required Senior Machine Learning Scientist I - GenAI Applications
26992
About the team:
This opening is for the GenAI Applications Team within the Data & AI Marketplace department.
The GenAI Applications team is responsible for designing and delivering agentic, ML-powered solutions for some of our most impactful products, including booking search experiences, trip planning, and trip helpfulness. The team builds AI-driven applications and conversational agents, such as chatbots and intelligent assistants, that significantly enhance the end-to-end customer experience.
Role Description:
As a Senior Machine Learning Scientist, you will work closely with engineers and to design, develop, and evaluate machine learning solutions for scalable, customer-facing GenAI applications. Your work will focus on researching, training, fine-tuning, and rigorously evaluating models leveraging LLMs, recommendation systems, and agent-based architectures, using state-of-the-art techniques. You will drive experimentation, define success metrics, and translate insights into impactful AI solutions that shape the future of intelligent travel products.
Key Job Responsibilities and Duties:
Explore and apply state-of-the-art techniques in multimodal machine learning.
Train innovative ML models (NLP, CV, LLM-finetuning), build algorithms, and engineering approaches to drive business impact..
Coding skills: ensure implementation of reusable frameworks (clean and scalable code).
Conduct data analysis with detailed metrics to evaluate models performance, labels quality, features exploration.
Work closely with machine learning engineers to ensure the model's latency/throughput meets product requirements and ensure deployment of your model to production.
Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions.

Required PhD Machine Learning Research Intern
27984
Role Description
About the team: The internship is open for PhD students in a machine learning related field like Planning with LLMs, Multilingual Training Adaptation, Recommender System and Ranking, Bidding, Synthetic Data Generation, Entity Representation Learning, Reinforcement learning and Classification.
Applicants are asked to attach with their CV the email address of their supervisor or co-supervisor. This email will be used to ask for a recommendation letter at a later date.
Please complete your application before March 31st, 2026. Participation in the internship program requires that you are located in Israel for the duration of the internship program (3 months)
Role Description:
As a Machine Learning Research intern at Booking.com in Tel-Aviv, you will have the opportunity to tackle real world problems by pushing the boundaries of the state-of-the-art, with the goal of publishing your work.
You will join us for a 3-month internship (preferred start date: May) focused on Data-Centric AI. In this project, you will tackle the "glass ceiling" of NLU performance: inherent ambiguity in annotation guidelines and label taxonomies. You will move beyond traditional noise reduction to investigate why human annotators disagree and how these disagreements corrupt "ground truth," directly influencing how we evaluate and improve state-of-the-art models.
Key Job Responsibilities and Duties:
Survey the Landscape: Conduct a comprehensive literature review to synthesize current approaches to data ambiguity, identifying the strengths and limitations of existing methods in handling label noise and taxonomy gaps.
Innovate & Design: Devise novel techniques and metrics to detect, quantify, and resolve inherent ambiguities within NLP datasets and annotation guidelines.
Experiment & Validate: Design and execute rigorous experiments to evaluate your proposed methods, measuring their impact on model reliability and ground truth accuracy.
Publish Findings: Summarize your conclusions and contribute to writing a research paper with the aim of submitting to a top-tier NLP conference (e.g., ACL, EMNLP).

Required Machine Learning Scientist II - GenAI Evaluation
20717
Role Description:
As a Machine Learning Scientist, your work will focus on the evaluation and optimization of generative AI systems. You will develop and fine-tune Judge LLMs to assess model outputs across a variety of tasks, design robust evaluation frameworks for agentic workflows, and build scalable pipelines for synthetic data generation. The team also plays a critical role in multilingual evaluation, enabling GenAI applications to support market expansion across all supported languages.
Key Job Responsibilities and Duties:
Develop and apply state-of-the-art techniques for evaluating generative AI systems, with a focus on agent workflows, multilingual output, and task-specific Judge LLMs.
Design and implement scalable evaluation pipelines, including synthetic data generation and benchmarking for model quality, relevance, and consistency..
Optimize and maintain Judge LLMs to assess outputs across dialog systems, Q&A, and trip planning use cases.
Conduct in-depth data analysis to define and track evaluation metrics, validate label quality, and explore performance across different languages and user scenarios.
Ensure the reliability, efficiency, and scalability of evaluation tools and frameworks in both offline and online environments.
Collaborate closely with ML engineers to integrate evaluation components into production pipelines, supporting continuous improvement of GenAI applications.
Work cross-functionally with product, research, and analytics teams to align evaluation strategies with business goals and user impact.

We are looking for an experienced Machine Learning Engineer to build and scale production-grade ML solutions, primarily on large-scale tabular automotive data for quality and cybersecurity products. Youll help define the engineering standards that enable the Data Science team to deliver at scale, with opportunities to own selected ML features end-to-end.
Youll work with massive (often live-streaming) automotive datasets and real-world constraints around latency, CPU, memory, and I/O. This is a highly collaborative role, and youll partner closely with data science and data engineering teams. Youll be driving technical alignment through clear communication and mentoring others via design and code reviews.
This role is full-time and based in Herzliya, Israel.
Responsibilities
Act as the DS engineering axis: drive designs with focus on performance (I/O, CPU, memory, latency, cost).
Lead heavy ML engineering efforts when needed (optimization, scaling, reliability), while collaborating with other team members and supporting them in their ML-related projects.
Own selected ML features and projects end-to-end, including DS work (EDA, features, modeling, evaluation) plus production delivery, monitoring and iteration.
Build production tabular - ML components: training, batch/near-real-time scoring, inference services, and shared libraries.
Set standards and tooling for profiling and preventing performance regressions.
Partner with the data engineers on data / ML contracts (schemas, SLAs, formats/partitioning) between pipelines and ML components.
Raise the bar via mentoring, documentation, and a strong code/design review culture, in the Data Science team.