דרושים » הנדסה » ML Platform Engineer

We are looking for an experienced ML/AI engineer to anchor our ML Platform team. You will lead the effort to productize our Large Language Models, transforming experimental code into robust, efficient services. Your goal is to build the infrastructure that allows our Data Scientists to move fast without breaking production.

What you'll be doing:
Model Development & Optimization

Fine-tuning & Engineering: Develop and fine-tune LLMs/classic models for specific medical tasks (condition detection, dialogue systems) using internal datasets.
Research to Production: Partner with Data Scientists to develop experimental code into scalable, production-ready modules.
Platform Engineering & Infrastructure

Pipeline Orchestration: Build and maintain complex ML workflows using Kubeflow Pipelines (KFP) or similar orchestration tools.
Internal Tooling: Develop and manage the internal Python ecosystem-libraries, SDKs, and utilities-that the Data Science team uses for daily development.
CI/CD & Automation: Write and maintain CI/CD scripts to automate the testing, versioning, and deployment of machine learning artifacts.
Production Standards & Integration

System Integration: Work with backend developers to integrate trained models/agents into the core application architecture.
Code Quality: Enforce high engineering standards through code reviews, ensuring that research code meets production reliability and maintainability requirements.
Requirements Engineering: Translate evolving data science requirements into concrete infrastructure and platform features.

We are seeking a highly skilled Senior Networking AI Platform Engineer to join our Applied Networking AI group. In this role, you will help design and develop cutting-edge AI solutions, integrating them seamlessly into a variety of products. Youll collaborate closely with multi-functional teams of data scientists, software engineers, and DevOps professionals to ensure the efficient deployment, monitoring, and optimization of machine learning (ML) models.
As a key contributor, you will drive the entire software development lifecycle-from conceptualization and architecture to implementation and production-while working closely with engineering teams to solve complex problems and help build a successful company practice.
What you'll be doing:
Lead the design, development, and deployment of robust software systems across different platforms and environments
Architect, design, and implement scalable and high-performance software solutions, handling complex requirements and integrating various subsystems
Ensure systems are maintainable, flexible, and well-documented, with an emphasis on performance and security
Adapt to new tools, technologies, and frameworks, and be capable of taking ownership of the development process from conception to deployment
Supply innovative ideas and solutions, driving continuous improvement in both code quality and system efficiency
Develop and maintain scalable infrastructure for handling and deploying security and networking ML models in production, ensuring high availability, scalability, performance.
Design and implement data pipelines to efficiently process and transform large volumes of data for training and inference purposes.
Optimize and fine-tune ML models for performance, scalability, and resource utilization, considering factors such as latency, efficiency, and cost.
Collaborate with data scientists and software engineers to operationalize and deploy ML models, including model versioning, packaging, and integration with existing systems.

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.

we are looking for a AI/ML/Software Engineer.
Here, you will tackle ever-evolving challenges and leverage the dynamic AI landscape to craft solutions that transform the cybersecurity industry. You'll have the chance to build cutting-edge models and AI systems for cybersecurity. You'll gain deep expertise in the fields of both AI and security, engaging with all facets of Generative AI techniques and their deployment in production environments. You will collaborate with a talented team of researchers, engineers and security experts, and play a pivotal role in developing groundbreaking AI solutions. Shape the future of AI for Security with us and make an enduring impact on AI adoption across the world!
Develop and train cutting-edge AI models for the security domain.
Develop a platform for AI data processing, training, fine tuning, evaluation and other AI related needs.
Develop agentic systems that automate and uplift security operations.
Author blog posts, white papers and research papers related to developments in AI for the security landscape.
Collaborate with cross-functional teams of researchers and engineers to translate research ideas into products.
Contribute to our groups culture as an early member of the team.

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.

Were hiring a ML Engineer to accelerate AI-driven innovation across Stamplis B2B SaaS platform.
Youll be at the forefront of building intelligent systems that power core product experiences and automate internal operations, driving efficiency, speed, and scale across the organization. This is a high-impact, hands-on role in a fast-growing, AI-first company where machine learning is a foundational pillar, not a bolt-on feature. You'll partner with product, engineering, and operations teams to design and implement powerful ML and LLM-based solutions that make a measurable difference.
What You Will Do:
Build Intelligent Systems: Design and develop ML/LLM-powered solutions that solve real-world challenges across Stamplis product and internal workflows.
Own Full Lifecycles: Take projects from concept all the way to production, including model training, evaluation, integration, and monitoring.
Leverage State-of-the-Art Tools: Work with leading frameworks like LangChain, Hugging Face, TensorFlow, and PyTorch to deliver cutting-edge functionality.
Collaborate Cross-Functionally: Partner with product managers, engineers, and stakeholders to embed AI capabilities into user-facing features and backend services.
Ship at Scale: Build and maintain scalable APIs and services, integrating best practices in CI/CD, observability, and cloud infrastructure.
Report with Impact: Share progress, challenges, and results clearly with technical and executive stakeholders.

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.

We are on an expedition to find you, someone who is passionate about creating intuitive, out-of-this-world production-grade AI systems and ML pipelines to join our AI group. You'll be responsible for designing, building, deploying, and maintaining production-grade AI systems and ML pipelines. Youll work closely with data scientists to translate research into scalable solutions and manage model deployment in both cloud and on-prem GPU environments.
:Responsibilities
Design, build, and deploy production-grade ML models, AI agents, and end-to-end pipelines across cloud and on-prem GPU environments.
Maintain and optimize ML systems for performance, scalability and reliability, including model validation, inference speed, and resource efficiency.
Develop monitoring and observability tools such as alerts and performance metrics to ensure system stability in production.
Create and integrate APIs for ML services within microservice-based architectures.
Drive adoption of best practices for CI/CD, observability, and reproducibility in ML systems.

Our data science team is seeking a highly skilled AI researcher with expertise in modern AI methods (e.g., multi-agent architectures, reinforcement learning on LLMs) and a passion for transforming theoretical challenges into real-world products.
In this role, you will leverage our unique clinical datasets to design and build groundbreaking solutions that provide medical benefits to millions of people. You will take full end-to-end ownership of your models - from ideation and research to production deployment and ongoing monitoring.
Working across both research and production environments, you should have proven experience in developing and deploying machine learning algorithms, along with strong interpersonal and collaboration skills. We value independent thinkers who are also proactive doers.

What Youll Be Doing:
Develop clinical AI models to support Ks clinic workflows, with a focus on patient-facing applications
Design and implement scalable, efficient pipelines for data preprocessing, information extraction, and model training
Stay up to date with the latest AI research and best practices, and translate them into production-ready solutions
Communicate complex technical concepts and solutions to non-technical stakeholders
Collaborate with MLE and AI engineering teams to deploy scalable, robust solutions

Required Data Science Engineer
About the Team
Our team focuses on delivering cutting-edge, AI-powered automated application security features within our ASPM product. We are a dynamic, agile team emphasizing seamless transitions from design to production, deployment, and ongoing monitoring. We excel at operationalizing machine learning, data science, and AI solutions into robust, secure, and high-performance security capabilities.
What You Will Do
Be a core member of a cross-functional AI innovation team, collaborating closely with software engineers, MLOps specialists, and analysts.
Design, build, and optimize end-to-end AI-driven features - from experimentation and model training to validation and deployment.
Develop production-grade code (Python and C#) to integrate advanced models and data-driven capabilities into our core products.
Continuously evaluate model performance and data quality in real-world settings, proactively driving improvements, retraining, and robust monitoring.