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Required Data Engineering Manager
Description
We are one of the most popular and downloaded apps in the world. Working with us provides a unique opportunity to influence hundreds of millions of our users and to be part of the journey that makes us a super-app. Our mission is to make peoples lives easier by enabling meaningful connections, from precious moments with family and friends, through managing business relationships to pursuing their passions.
As an Engineering Manager in the data department, youll build and scale our data platform and data apps that powers our business insights. Youll design and implement robust pipelines to process billions of daily records, leveraging cutting-edge cloud technologies to transform data into actionable intelligence.
If youre passionate about data engineering and driving business growth through insights, wed love to hear from you!
Responsibilities
Lead and grow Data Engineering and Machine Learning teams in a high-scale environment (tens of billions of events per day).
Own the design and evolution of a self-service data platform enabling internal teams to easily build, ship, and consume data products.
Architect and scale batch and streaming pipelines powering core business and ML use cases.
Drive production ML systems end-to-end (recommendation, ranking, prediction) with direct business KPI impact.
Ensure reliability, scalability, and observability of large-scale data and ML systems in production.

Senior Data Scientist
Description
We are one of the most popular and downloaded apps in the world. Working with us provides a unique opportunity to influence hundreds of millions of our users and to be part of the journey that makes us a super-app. Our mission is to make peoples lives easier by enabling meaningful connections, from precious moments with family and friends, through managing business relationships to pursuing their passions.
Connecting people across the world is a complex problem with many machine-learning applications. The purpose of this role is to implement mathematical models and algorithms to solve complex business problems in recommendations and classification. Successful outcomes will significantly impact our hundreds of millions of daily active users around the globe.
As a Senior Data Scientist, you will work in a highly collaborative environment with extensive amounts of data to research and develop deep learning models in the domains of dating, moderation and content segmentation and apply them to tasks such as recommendation systems and analytics at a high scale.
Responsibilities:
Work with management and partner teams to design and implement solutions in recommender systems for given objectives.
Lead technical efforts to improve the performance of deep learning models and propose initiatives to impact company goals directly.
Autonomously find solutions to complex problems in social network recommendations and understand the data generation process and challenges with the data.
Analyze and leverage the extensive data received from our application to enhance model performance and accuracy.

We're looking for a Data Platform Engineer to own and scale the Kubernetes infrastructure powering our large-scale data processing platform.

This is a hands-on role at the intersection of infrastructure and data engineering. You'll operate Kubernetes clusters running thousands of nodes, supporting workloads like Spark, Airflow, and remote shuffle services. Your focus: making distributed data workloads reliable, cost-efficient, and performant at scale.

This is not a traditional DevOps or SRE role. You won't be building CI/CD pipelines or managing web services. Instead, you'll be deep in Spark executor scaling, shuffle optimization, batch scheduler tuning, and capacity planning for clusters that process massive datasets daily.

If you've tuned Spark on Kubernetes at scale, wrestled with shuffle storage bottlenecks, or optimized batch scheduling across thousands of concurrent pods - this role is for you.

WHAT YOU'LL DO:
Operate and scale Kubernetes clusters with thousands of nodes supporting large-scale Spark and data processing workloads.
Manage and optimize Apache Spark on Kubernetes - executor autoscaling, driver scheduling, resource tuning, spot instance strategies.
Deploy and tune remote shuffle services (e.g., Apache Celeborn) to handle shuffle data at scale across multiple availability zones.
Operate and improve self-hosted Apache Airflow infrastructure on Kubernetes
Configure and optimize batch schedulers (e.g., YuniKorn, Volcano) for gang scheduling, fair-share queuing, and resource prioritization.
Drive cost optimization across large compute fleets - spot vs. on-demand strategies, node right-sizing, autoscaling policies, local SSD utilization.
Support and collaborate with Data Engineering teams on workload. performance, resource allocation, and infrastructure requirements.
Manage infrastructure-as-code (Terraform) and GitOps deployments (ArgoCD, Helm) for data platform services.
Integrate with managed data platforms (e.g., Databricks) and cloud storage for hybrid processing architectures.

Required Staff Data Scientist, GenAI and Agentic SOC
About the job
Our software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to our needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. You will anticipate our customer needs and be empowered to act like an owner, take action and innovate. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward. we accelerate every organizations ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage our cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to us as their trusted partner to enable growth and solve their most critical business problems.
Responsibilities
Drive the technical direction and execution for all machine learning (ML) and AI initiatives within the team, acting as the central tech lead for our Generative AI transformation.
Design, build, and scale the core infrastructure and pipelines necessary to develop, fine-tune, and deploy autonomous security agents (like our alert response agents) into production.
Create testing and evaluation methodologies to continuously measure the safety, accuracy, and performance of LLMs and agentic workflows in high-stakes security environments.
Partner closely with software engineering, product, and UX teams to seamlessly blend deterministic playbooks with dynamic AI intelligence, ensuring a flawless experience for SOC analysts.
Mentor peers, set the standard for ML operational excellence, and foster a startup-like culture of rapid, safe experimentation and innovation.

Required Senior ML Data Engineer
Ramat Gan
Full time
The AI Engineering group builds modern infrastructure and solutions that improve how algorithms are developed.
We are a small, independent team of experienced engineers with a mix of skills in algorithms, software, and infrastructure. We work in a DevOps style and build cross-team solutions that support research and development of advanced perception algorithms.
Our flagship project is a unified AV dataset used to train and evaluate next-generation models. We take large volumes of multi-camera video, object labels, HD maps, and sensor data from across the organization, and turn it into a curated, high-quality training set - at scale.
We are looking for someone who brings ML and computer-vision depth to the team - someone who can help shape the intelligence layer that decides what data is worth training on.
What will your job look like:
Work collaboratively with shared ownership. Your focus area will be the curation and ML side of our data pipeline, but you will contribute across the full stack alongside the rest of the team.
Build and improve the curation pipeline - from vision-model embeddings and scene detection, through VLM-based scene analysis, to scoring, deduplication, and sampling that produces a balanced and diverse dataset.
Run and optimize GPU inference at scale (embedding extraction, VLM inference) across thousands of driving sessions using workflow orchestration.
Develop scoring and sampling strategies that ensure rare but important scenarios (night driving, adverse weather, hazardous situations) are well-represented in the final dataset.
Work with algorithm teams to understand what data gaps hurt model performance and translate those into curation criteria.
Build validation and diagnostics that measure dataset quality - not just pipeline health, but whether the data is actually good for training.
Contribute to the core dataset SDK, converter, and 3D-geometry tooling (camera projection, calibration, coordinate transforms).

We are looking for a Data Engineer to join our Object Detection group and play a key role in managing and enabling data workflows that power our computer vision algorithms. Our team generates and processes large volumes of diverse data from multiple sources, and we need someone who can bridge the gap between raw data and algorithmic innovation. Working alongside algorithm developers and researchers, you'll be responsible for building robust data pipelines, ensuring data quality, and making complex datasets accessible and actionable for our detection models. This role requires both strong coding abilities and a deep understanding of data - you'll need to think like an engineer while understanding the unique needs of a research and development-focused algorithmic group.
What will your job look like:
Work on large-scale systems handling diverse data from multiple sources
Build and maintain robust data pipelines that enable algorithmic development
Evaluate various optional technologies and take part in decision-making
Own key features and services within the data infrastructure
Ensure data quality and accessibility for detection models and research needs
Work onsite in Jerusalem at least one day a week.

We are honored to serve more than 1,800 customers, which includes 40 percent of all Fortune 100 companies including Siemens, Airbus, SalesForce, Stellantis, Adidas, Wal-Mart and Sanofi.
What are we looking for?
Director, Business Intelligence & AI to join our Business Applications organization. This is a senior individual contributor role with a director title and light team leadership (5 people), sitting at the intersection of data strategy, architecture, and execution. You'll own the data foundation that powers our company's most critical business decisions - from GTM and revenue analytics to operational intelligence across the organization.
You'll report to the VP of Business Applications and work closely with stakeholders across Finance, Sales, Marketing, Customer Support, Product, R&D, and HR to ensure our data assets are trustworthy, scalable, and increasingly AI-ready
What will you do?
Architecting the data foundation that makes it possible for business users to ask questions in plain English and get trusted, real-time answers.
Define and execute a strategy to migrate from traditional BI (dashboards, scheduled reports) toward AI-powered, natural language interfaces for business intelligence
Architect the data layer that enables LLM-based querying - including semantic layers, vector stores, knowledge graphs, and metadata enrichment - so that AI agents can reliably reason over company data
Design and own a scalable, AI-ready data architecture - warehouse/lakehouse structure, semantic layer, and data contracts - that supports both analytical and AI/ML workloads
Build robust data modeling foundations (clean entities, clear metrics definitions, consistent taxonomies) that LLMs can reason over accurately
Serve as a trusted advisor to business leaders across GTM, Customer Success, Finance, and Product - proactively engaging them to understand their decision-making needs
Translate ambiguous business questions ("why is churn spiking in the mid-market?") into well-defined data problems, and then into scalable, AI-powered solutions
Build strong relationships with stakeholders at all levels - from individual analysts to VP and C-suite - and maintain a prioritized roadmap that reflects real business value