לוח משרות דרושים הייטק דאטה

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).

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 looking for a Senior Data Scientist.
As a Sr. Data Scientist, you will be spearheading the analysis of tens of thousands of patient-therapist conversations, working closely with our leadership, product, and clinical experts team, to derive insights that directly improve therapy sessions for thousands of people.
This is a unique opportunity to join a startup with real impact on the wellbeing and mental health of thousands, applying cutting edge technology for solving a meaningful human problem with huge financial and psychological implications.

We seek an experienced and highly skilled Senior Engineer to join our Data Platform Team. This is an exciting opportunity to work on cutting-edge technologies, drive innovation, and play a key role in designing and implementing the companys data infrastructure and architecture.

What you'll do

Database Infrastructure Development - Design, develop, and maintain scalable and reliable self-serve database infrastructure, including PostgreSQL, MongoDB, SingleStore, and AWS Aurora.

Big Data Infrastructure Development - Design, develop, and maintain scalable and reliable self-serve big data infrastructure, including cloud-agnostic data lakehouse, distributed databases, and platform-powered data pipelines.

End-to-End Ownership - Take responsibility for the entire lifecycle of data infrastructure, from DevOps (Terraform, Kubernetes) to application-level components (infrastructure libraries, core components in data pipelines).

Architecture Leadership: Collaborate with the Architecture group to define and maintain the companys core architectural vision.

Partner with development teams, providing technical guidance, best practices, and support from the design phase to production deployment.

Innovation and Exploration - Work on diverse and impactful projects across different layers of the tech stack, exploring new technologies and approaches to improve reliability, efficiency, and scalability.

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

one of the world's most impactful shopping recommendation companies, is searching for a talented Data Engineer to join our innovative team. For the past decade, we've served tens of millions of users globally, and we're looking for a passionate individual to help us continue our journey of making an impact.
What You'll Own:
Data Infrastructure
Build and operate data pipelines on GCP using Cloud Workflows, Cloud Functions, Cloud Scheduler, and BigQuery.
Create, Transform and Manage Datasets (Python, DBT,..)
Design and implement API integrations to pull data from affiliate networks, ad platforms, and internal services.
Ensure pipeline reliability, observability (monitoring, alerting, SLA management), and scalability.
Data Quality & Trust
Implement tests, freshness checks, and anomaly detection across all critical models.
Lead incident triage and root-cause analysis when data issues arise; build systems that catch problems before stakeholders do.
Maintain clear documentation of data contracts, schema definitions, and transformation logic.
ML & Data Science Collaboration
Work hand-in-hand with Data Scientists building ML models - ensuring feature tables, training datasets, and inference pipelines are clean and reproducible.
Build the data layer that feeds ML model training and evaluation, including feature stores and labeled datasets.

We're in search of an exceptional hands-on Data Scientist who can bridge data infrastructure, fraud analytics, and machine learning to enhance our fraud detection capabilities. This role requires someone who can drive projects from ideation to implementation, leading these efforts while thinking strategically about how we detect and prevent fraud at scale. Our ultimate goal is to equip our clients with resilient safeguards against chargebacks, empowering them to safeguard their revenue and optimize their profitability. Join us on this thrilling mission to redefine the battle against fraud.
Your Arena:
Develop and enhance fraud detection models from concept to production, focusing on data normalization, network analysis, and risk scoring.
Take ownership to identify, analyze, and implement solutions for fraud patterns and behavioral anomalies.
Design, build and maintain data science pipelines and fraud intelligence systems to improve detection accuracy.
Collaborate with product, engineering, and risk teams to implement fraud prevention strategies.
Own projects end-to-end - balancing short-term wins with long-term strategy.

We are looking for a Head of Data Analytics to build and lead the companys data platform and data strategy. The Head of Data will own our entire data platform, research, and data strategy.
This includes building the data infrastructure that powers the company, establishing reliable company-wide metrics, enabling data-driven decision making, and turning our dispute and payments data into a long-term product advantage.
This is a foundational leadership role responsible for making data a core capability across the organization.
The Head of Data Analytics will work closely with Product, Engineering, Operations, and Leadership to ensure data becomes a central part of how the company builds products and runs the business.
Your Arena:
Data Platform and Architecture - Design and lead the development of our modern data infrastructure:
Building scalable data pipelines across product, payments, disputes, and internal systems
Developing a centralized data warehouse as the companys single source of truth
Ensuring data quality, reliability, and observability
Establishing data governance and best practices
Company Metrics and Decision Intelligence - Define and standardize the core metrics used across the company:
Establishing a consistent metrics framework across product, revenue, and operations
Building dashboards used by leadership and operational teams
Ensuring reliable and accessible reporting across the organization
Supporting data-driven decision making at the leadership level
Product Data and Insights - Partner with Product and Engineering to leverage our unique dispute and payment data:
Identifying patterns and insights across disputes and recovery performance
Enabling data-driven product development
Supporting predictive capabilities and automation in dispute management
Helping build product features powered by data insights
Operational Intelligence - Provide visibility and insights into dispute operations and business performance:
Building operational analytics dashboards
Supporting forecasting and capacity planning
Identifying automation opportunities
Improving operational performance through data insights
Building the Data Organization - Build and lead the companys data team:
Hiring and managing data engineers, analytics engineers, and analysts
Establishing strong data culture and best practices
Enabling teams across the company to use data effectively
Ensuring data privacy, security, and governance standards
What Success Looks Like - Within the first 12-18 months:
We operate from a trusted company-wide data platform
Leadership and teams rely on consistent and reliable metrics
Product teams leverage dispute data to build data-powered features
Operational performance becomes clearly measurable and optimized
A strong Data organization is established and growing.

The role is both highly analytical and highly customer-facing. It requires a proactive mindset, strong influencing skills, and the ability to translate deep, data-driven fraud insights into clear, practical recommendations that help merchants make confident decisions. You will hire and develop analysts, define how the team operates, and play a central role in shaping how delivers fraud expertise in a high-performance, growth-oriented environment.

Key responsibilities

Build and lead a Fraud Analytics team focused on merchant-specific fraud strategies

Hire, mentor, and develop fraud data analysts while setting a high analytical and professional bar

Own complex fraud analyses and strategy recommendations end-to-end, from investigation to deployment

Design, test, and deploy fraud rules and data-driven strategies, leveraging machine learning models

Define clear success metrics and track performance against them

Work closely with Commercial teams in customer-facing contexts, supporting merchant conversations with clear, structured insights

Partner with Product, Data Science, Engineering, and Operations to translate merchant needs into scalable fraud capabilities

Influence across the organization and drive continuous improvement in how fraud strategy and analytics are delivered

AI Transformation Lead -HO About the Role We are looking for a highly teהבגchnical and hands-on AI Transformation Lead to drive the implementation and adoption of AI across the organization. This is a builder role, not a strategy-only position. You will be responsible for identifying high-impact opportunities, designing and implementing AI-powered solutions, and integrating them into our existing systems and workflows across R&D, Support, Sales, and Operations. The ideal candidate combines a strong engineering background with the ability to translate business needs into scalable, production-ready solutions - and ensure they are actually adopted and deliver measurable impact.
Role Mission Lead the companys AI transformation end-to-end through practical implementation - building, integrating, and scaling AI-driven workflows and internal tools that improve efficiency, quality, and business performance across all departments.
What Youll Do Build & Implement AI Solutions (Core Focus)
* Design, build, and deploy AI-powered workflows, automations, and internal tools
* Develop rapid POCs and evolve them into production-ready solutions
* Work hands-on with LLMs, APIs, and automation frameworks
* Ensure solutions are scalable, maintainable, and aligned with system architecture
Integrate with Systems & Architecture
* Integrate AI solutions into existing platforms ( CRM, support systems, internal tools, data flows)
* Design robust integrations using APIs and orchestration layers
* Ensure alignment with engineering standards, security, and data integrity
Drive Cross-Company Impact
* Identify high-value use cases across R&D, Support, Sales, Domain, and Operations
* Translate business workflows into efficient, AI-driven processes
* Partner with stakeholders to ensure solutions solve real problems
Own Adoption & Impact
* Ensure solutions are used, not just built
* Define and track KPIs (efficiency, time saved, quality improvements, adoption rates)
* Continuously iterate based on feedback and real-world usage
Enable & Standardize
* Establish best practices for AI usage across the company
* Guide teams on how to effectively use AI tools in their daily work
* Act as the internal expert for AI workflows, tools, and capabilities

we are looking for a Senior Data Engineer I.
As a Senior Data Engineer, youll collaborate with top-notch engineers and data scientists to elevate our platform to the next level and deliver exceptional user experiences. Your primary focus will be on the data engineering aspects-ensuring the seamless flow of high-quality, relevant data to train and optimize content models, including GenAI foundation models, supervised fine-tuning, and more.
Youll work closely with teams across the company to ensure the availability of high-quality data from ML platforms, powering decisions across all departments. With access to petabytes of data through MySQL, Snowflake, Cassandra, S3, and other platforms, your challenge will be to ensure that this data is applied even more effectively to support business decisions, train and monitor ML models and improve our products.
Key Job Responsibilities and Duties:
Rapidly developing next-generation scalable, flexible, and high-performance data pipelines.
Dealing with massive textual sources to train GenAI foundation models.
Solving issues with data and data pipelines, prioritizing based on customer impact.
End-to-end ownership of data quality in our core datasets and data pipelines.
Experimenting with new tools and technologies to meet business requirements regarding performance, scaling, and data quality.
Providing tools that improve Data Quality company-wide, specifically for ML scientists.
Providing self-organizing tools that help the analytics community discover data, assess quality, explore usage, and find peers with relevant expertise.
Acting as an intermediary for problems, with both technical and non-technical audiences.
Promote and drive impactful and innovative engineering solutions
Technical, behavioral and interpersonal competence advancement via on-the-job opportunities, experimental projects, hackathons, conferences, and active community participation
Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions. Provide technical guidance and mentorship to junior team members.
21679

we are looking for a Data Engineer.
As a Data Engineer, youll collaborate with top-notch engineers and data scientists to elevate our platform to the next level and deliver exceptional user experiences. Your primary focus will be on the data engineering aspects-ensuring the seamless flow of high-quality, relevant data to train and optimize content models, including GenAI foundation models, supervised fine-tuning, and ore.
Youll work closely with teams across the company to ensure the availability of high-quality data from ML platforms, powering decisions across all departments. With access to petabytes of data through MySQL, Snowflake, Cassandra, S3, and other platforms, your challenge will be to ensure that this data is applied even more effectively to support business decisions, train and monitor ML models and improve our products.
Key Job Responsibilities and Duties:
Rapidly developing next-generation scalable, flexible, and high-performance data pipelines.
Dealing with massive textual sources to train GenAI foundation models.
Solving issues with data and data pipelines, prioritizing based on customer impact.
End-to-end ownership of data quality in our core datasets and data pipelines.
Experimenting with new tools and technologies to meet business requirements regarding performance, scaling, and data quality.
Providing tools that improve Data Quality company-wide, specifically for ML scientists.
Providing self-organizing tools that help the analytics community discover data, assess quality, explore usage, and find peers with relevant expertise.
Acting as an intermediary for problems, with both technical and non-technical audiences.
Promote and drive impactful and innovative engineering solutions
Technical, behavioral and interpersonal competence advancement via on-the-job opportunities, experimental projects, hackathons, conferences, and active community participation
Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions. Provide technical guidance and mentorship to junior team members.
20718

we are looking for a Senior Data Engineer.
Responsibilities:
Design and maintain scalable data pipelines with Spark Structured Streaming.
Implement Lakehouse architecture with Apache Iceberg.
Develop ETL processes for data transformation.
Ensure data integrity, quality, and governance.
Collaborate with stakeholders and IT teams for seamless solution integration.
Optimize data processing workflows and performance.