דרושים » תוכנה » ML Engineer - AI Infra Group

We are on an expedition to find you, someone who is passionate about creating intuitive, out-of-this-world production-grade AI infrastructure. This group builds scalable, high-performance AI systems for internal users and external customers, designed to run seamlessly across cloud and on-premise environments using the latest hardware advancements.
:Responsibilities
Design, build, and maintain scalable Kubernetes-based infrastructure for ML workloads across on-premise and cloud environments
Architect hybrid infrastructure solutions enabling seamless model flow from on-premise training environments to cloud-based inference deployments
Implement model registry and artifact management strategies that support cross-environment synchronization, versioning, and governance
Design secure, efficient data and model transfer mechanisms between on-premise and cloud (networking, storage replication, caching strategies)
Implement and manage GPU scheduling, resource allocation, and cluster autoscaling for heterogeneous compute environments
Build and maintain CI/CD pipelines for ML systems, including model versioning, testing, and promotion across environments
Develop observability solutions (logging, monitoring, alerting) for ML infrastructure across hybrid deployments
Collaborate with ML Engineers to define infrastructure requirements and SLAs for training and serving workloads

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 Machine Learning Engineer, youll work on cutting-edge
code-focused LLMs and AI agent systems
that power our companys next-generation developer platform. Youll be at the center of research, model training, and productionization of intelligent systems that understand software deeply, collaborate with developers, and help automate engineering workflows end-to-end. Your work will immediately impact millions of engineers worldwide.
Responsibilities:
Push LLM Innovation: Research, design, and fine-tune domain-specific LLMs for code generation, refactoring, debugging, and multi-turn reasoning.
Agent-Oriented Development: Build multi-agent coding systems that integrate retrieval-augmented generation (RAG), code execution, testing, and tool use to create autonomous, context-aware coding workflows.
Production-Grade AI: Own the training-to-inference pipeline for large code models-optimize inference with quantization, distillation, and caching techniques.
Rapid Experimentation: Prototype and validate ideas quickly; leverage reinforcement learning, human feedback, and synthetic data generation to push accuracy and reasoning.
Cross-Functional Collaboration: Partner with product, engineering, and design teams to ship AI-powered features that help developers focus on high-impact work.
Scale the Platform: Contribute to distributed training, scalable serving systems, and GPU/TPU-efficient architectures for ultra-low-latency developer tools.

our company has been at the forefront of the deep learning revolution, pioneering innovations that have transformed the entire field. As the leading provider of GPUs and AI computing platforms, our company has empowered researchers and engineers worldwide to accelerate breakthroughs in artificial intelligence.
We seek a versatile Senior Software Engineer who is passionate about performance optimization and generative AI. Our team builds software solutions that enable efficient inference on the latest and greatest generative AI models. We tackle problems on all levels of the stack-from server-level request batching to GPU kernel fusion-and collaborate with teams across diverse disciplines to push our company's hardware to its full potential.
What youll be doing:
Cooperate with research teams to onboard new LLMs and VLMs into our company's opensource AI runtimes
Optimize inference workloads using sophisticated profiling and simulation tools
Build SOLID, extendable inference software systems, and refine robust APIs
Implement and debug low-level GPU code to harness the latest HW features
Own end-to-end inference acceleration features and work with teams around the world to deliver production-grade products.

Were looking for a highly motivated AI Developer to help design, build, and deploy intelligent agentic systems across our product ecosystem. In this role, you'll work at the intersection of machine learning, backend systems, and modern frontend technologies to deliver AI-first features that feel magical to users.

This is a hands-on, cross-functional role ideal for engineers who love building full-fledged features-from data pipelines and LLM orchestration to intuitive UI experiences-with a strong product mindset.

Responsibilities:
AI Agent Design & Integration
Design and implement autonomous or semi-autonomous agents using LLMs (e.g., OpenAI, Anthropic, open-source models).
Work with prompt engineering, RAG pipelines, and tool/plugin integrations to enable agents to interact with internal and external systems.
Build scalable agent runtimes and orchestration layers (e.g., LangChain, Semantic Kernel, ReAct-based agents).
Fullstack Product Development
Own full-stack features end-to-end: from backend APIs and data models to React-based frontend interfaces.
Integrate AI/agent capabilities into customer-facing products with clean UX and measurable performance.
Collaborate closely with design, product, and data teams to bring ideas from concept to production.
Systems & Infrastructure
Build and maintain backend services and pipelines that support AI agents, including vector search, embeddings, function calling, and observability.
Optimize inference flows for performance and cost, potentially using streaming, caching, or local model inference.
Ensure systems are secure, reliable, and compliant with InfoSec standards.
Experimentation & Continuous Improvement
Rapidly prototype and iterate on new AI capabilities and user experiences.
Analyze performance and usage metrics to drive product and model improvements.
Stay up to date with the evolving AI toolchain and emerging agent architectures.

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.

As a Senior MLOps Engineer , you will design, build and scale our machine learning infrastructure. You will lead the end to end model lifecycle, from data and training to deployment and monitoring, ensuring reliable and high performance ML systems in production. You will work closely with our research and engineering teams to bring advanced AI models into production and help shape the future of our AI capabilities.

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 mission - this role is for you.
: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.

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.