דרושים » הנדסה » MLOps engineer

For many of us theres that one podcast we never miss, and video content is part of our daily routine, whether its professional or personal. But how many of us truly understand the effort that goes on behind the scenes? Here at Riverside, we know it well. Thats exactly why we built an AI-powered platform that helps content creators, podcasters, marketeers, and more at major brands like Netflix, Disney, Google, and Microsoft to create high-quality content with ease. Riversides technology streamlines the entire content creation process, turning ideas into professional-grade content with the highest production standards, without requiring expensive equipment or external services. The secret? AI-driven tools that replace traditional production roles like editing, directing, and design, automating the entire process at the click of a button.
What Will Make You Stand Out?

* 6+ years of production-grade Python development experience.
* Strong background in distributed systems: Youve built and debugged complex, event-driven architectures (e.g., Kafka, microservices).
* Expertise in data Engineering at scale: Experience building massive data pipelines and architecting data Lakes (S3) with compute layers like Athena for large-scale analysis.
* Deep understanding of the MLOps lifecycle: Experience taking models from training to deployment, including versioning and performance monitoring.
* Experience with containerized environments, microservices, and Kubernetes.
* Experience with workflow management frameworks (Temporal, Airflow) and asynchronous programming.
* Experience with cloud platforms (AWS preferred) and model-serving frameworks (Triton, VLLM/SGLang, Ray Serve).
* A love for exploring new tech and the drive to implement modern frameworks that move the needle.
Bottom line? If you wanna take part in transforming how people and businesses share their stories globally, Riversides your place. The work is challenging, the culture is fast-paced, and the people are exceptionally brilliant. And if thats not enough, we guarantee that your ideas will genuinely make an impact.

we are looking for an AI Engineer to lead our MLOps team and take full ownership of our machine learning infrastructure used to train, evaluate, and deploy our state-of-the-art computer vision models. In this role, you will work closely with other team leads and Product to plan and build the future of core AI platform. You will help define technical direction, make long-term architectural decisions, and translate company goals into executable plans, directly impacting ability to scale its AI capabilities and the overall success of the company.
Responsibilities

Manage and lead the MLOps team, including mentoring, professional development, and day-to-day technical guidance
Plan and own quarterly roadmaps, sprint planning, task breakdown, and execution, ensuring alignment with company and product priorities
Design, build, maintain, and evolve ML infrastructure for training, evaluation, and deployment of computer vision models
Own the end-to-end ML lifecycle in production, including experimentation workflows, training orchestration, model versioning, deployment, and monitoring
Balance delivery and technical excellence by making architectural decisions and enforcing engineering best practices
Work closely with research, data, product, and platform teams to move models reliably from research to production
Actively contribute code to critical components and review design and implementation across the team
Evaluate and introduce new technologies and tooling to improve scalability, reliability, and developer productivity

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.

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.

We are looking for a highly skilled and motivated Senior II Software Engineer to join the Operational Experience engineering team. The team is part of the Customer Experience group, which is responsible for the platform, tools, and customer-facing experiences that power how our customers interact with our ecosystem. This is a high-impact, hands-on role, in which youll be working closely with product managers, designers, customer-facing teams, and engineering partners across the company.

You will operate at the intersection of backend engineering, data-intensive systems, platform development, and customer experience. The ideal candidate brings strong expertise in Node.js and TypeScript, along with deep experience working with large-scale data stores, event-driven pipelines, data models, and high-throughput infrastructure. You will work closely with cross-functional partners to design and implement robust backend services, data-access patterns, and operational workflows that power the portal and internal tools. As we invest heavily in Agentic AI, you will also play a central role in shaping and implementing AI-driven capabilities across the platform. While the role is primarily backend, you will occasionally contribute across the full stack when it supports end-to-end delivery.

If you enjoy owning complex problems end to end, improving systems at scale, and building experiences that bring real value to customers, we would love to meet you.

What you'll be doing:
Drive technical direction and architecture within the OX team and across the broader CX organization. You will proactively identify opportunities to improve performance, resilience, cost, scalability, and developer experience, primarily in backend systems but with influence across the stack.
Lead the development of AI-driven and Agentic AI capabilities. Define how LLMs integrate into our platform, build AI-powered workflows, and establish strong engineering patterns for safe and reliable adoption.
Own and evolve the data foundations behind the portal. Optimize pipelines, improve data quality and freshness, and design resilient data-access patterns across Snowflake, Elasticsearch, Kafka, Redis, MySQL, and related systems.
Work closely with product, design, customer-facing teams, and partner engineering groups. Turn ambiguous problems into clear execution plans and ensure alignment with customer and business goals.
Shape shared standards and platform best practices. Guide other teams on backend services, data integration patterns, portal development approaches, and AI-enabled workflows.
Mentor and elevate engineers across the CX group. Promote engineering excellence, share knowledge openly, and help teams adopt effective modern development practices.
Own delivery of high-impact initiatives. Contribute hands-on when needed, remove blockers, maintain execution momentum, and drive projects from concept to production.

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 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 looking for an experienced Senior Backend Engineer to join our Browse Management Core team.

As part of our core team, youll design and implement high-impact backend services, collaborate with frontend, DevOps, and product teams, and play a key role in shaping the next generation of Browse Management architecture.

Youll help build and evolve the backend infrastructure that powers our cloud-native security platform, delivering advanced protection, observability, and scalability at global scale.

If youre passionate about solving complex backend challenges, scaling distributed systems, and driving innovation in cybersecurity, this is the place for you.

Key Responsibilities
Design, develop, and maintain cloud-native backend services supporting a large-scale SaaS security platform.
Build scalable, secure, and resilient architectures using modern AWS services (EKS, ECS, EC2, RDS, SQS, CloudFront, IAM).
Develop and optimize RESTful and event-driven APIs, backend integrations, and data flows.
Work in a microservices-based environment, ensuring high availability, observability, and performance.
Collaborate closely with frontend, DevOps, and product teams to deliver end-to-end solutions.
Implement monitoring, alerting, and reliability best practices for production environments.
Write clean, maintainable, and testable code, contributing to best practices and design standards.
Participate in architecture and design reviews, influencing the technical direction of the platform.

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 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