דרושים » תוכנה » Senior Machine Learning Engineer I - GenAI Applications

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 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.

In this role, you'll be responsible for designing and implementing evaluation, validation and optimization of GenAI systems. You will define, design and develop LLMs as judges to evaluate task and system outputs across multiple applications, create datasets for benchmarking and evaluation and help design robust and scalable evaluation pipelines for both onine and offline GenAI systems.
:Responsibilities
Design, develop and apply state-of-the-art techniques for evaluating and validating AI agents and/or workflows.
Develop and implement LLM-as-a-Judge (or similar) for different tasks and roles for GenAI systems and tools.
Design and implement evaluation pipelines and benchmark datasets for evaluating model quality, relevance and system consistency for various applications.
Optimize and maintain judge LLMs to evaluate outputs for different use cases such as chatbots, RAG systems, cybersecurity experts and investigators.
Define evaluation KPIs and metrics for both models, systems and tools.
Validate and optimize datasets for various use cases.
Ensure the reliability, efficiency, and scalability of evaluation tools and pipelines for both online and offline use cases.
Work closely with AI/ML engineers to make evaluations a part of the production pipelines of GenAI applications.
Collaborate with cross-functional teams including product, research and data science.
Stay up to date with the latest developments in AI, machine learning, focusing on LLMs, exploring how emerging technologies can be applied to improve our evaluation and validation pipelines.

We are looking for an ML Engineer / MLOps Tech Lead to promote machine learning engineering excellence. Someone who is passionate about building scalable, high-quality data products and processes, while ensuring production systems maintain strong real-time performance observability.
You will focus on designing and maintaining the core infrastructure that empowers the Machine Learning Engineers working within Data Science product teams. Youll collaborate closely with stakeholders across data science, product, and engineering, playing a pivotal role in driving the business by architecting and enabling the infrastructure for machine learning model development, serving, and lifecycle management-the foundation of our product.
Responsibilities:
Partner with MLEs in Data Science product teams and key stakeholders to design and maintain infrastructure for:
Data wrangling - supporting and enabling data requirements for research, training, validation, and testing.
End-to-end ML delivery - enabling model performance development, training, validation, testing, and version control.
Drive engineering best practices including code and model versioning, CI/CD pipelines, rollout strategies, and disaster recovery procedures.
Build and support monitoring and observability tools - dashboards, alerts, and performance tracking of models in production.
Lead architecture projects such as:
Feature Store - centralizing feature engineering and serving across teams.
Vector Databases - enabling large-scale embedding storage and retrieval for advanced ML applications.
GPU Cluster Scaling - optimizing distributed training and inference infrastructure.
Collaborate with product, data science, and engineering teams to solve complex problems, identify trends, and create opportunities through robust ML infrastructure.

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.

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.

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.

We are currently looking for a Senior Machine Learning Engineer to design and implement scalable and integrate machine learning solutions into production. You will work with modern technologies like Node.js, Python, Kafka, MongoDB, and OpenSearch, AirFlow, while driving architectural decisions and CI/CD best practices. This role combines backend engineering excellence with ML deployment expertise.

The Senior Machine Learning Engineer will report directly to the Director of R&D.

Your key responsibilities will include:

Architect and develop MLE services using Python, ensuring high performance, reliability, and scalability.
Collaborate with Data Science teams to productionize ML models (model serving, monitoring, retraining pipelines).
Build and maintain CI/CD pipelines for automated testing, deployment, and monitoring.
Design and implement system architecture for distributed, event-driven systems.
Integrate and optimize Kafka for real-time data streaming and event processing.
Design and manage MongoDB schemas and queries for optimal performance.
Implement and maintain OpenSearch clusters for search and analytics use cases.
Ensure security, observability, and fault tolerance across all services.
Mentor team members and contribute to engineering best practices.

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