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We are looking for a Senior Data Scientist / Applied AI Researcher with deep expertise in NLP and LLM-based systems, and the ability to operate across the full lifecycle, from problem formulation and research design to production deployment and real-world iteration.
You wont be working on isolated experiments. Youll be part of a multidisciplinary team that owns the system end-to-end and is responsible for how it behaves under real production traffic.
This role requires strong scientific discipline and the maturity to balance innovation with practical constraints.
What youll do:
End-to-End Ownership: Lead AI security solutions from problem formulation and research design through model development, evaluation, and production deployment. Ensure solutions are measurable, reliable, and effective under real-world traffic.
Drive Technical Direction: Take responsibility for ambiguous AI security problems, propose thoughtful approaches, and drive structured experimentation and deep model analysis.
Build for Scale: Design systems that are not only accurate, but scalable, efficient, and robust.
Staying Updated: Continuously explore advances in NLP and LLM-based systems, and critically assess what is worth integrating into a production environment.

our mission is to make the pursuit of creative hobbies simple and fun for all. our top-grossing apps put us on the fast track to building the world's largest consumer subscription service for creative learning. with over a million monthly downloads and hundreds of thousands of daily learners, we're building the ai creative companion for every home.
what data leads do here:
we are looking for a data lead to own end-to-end data -driven business and product analysis. the product data lead impacts all data aspects of the product teams ( data modeling, analytics, tests, visualization, data engineering, ml), influencing critical product decisions.
who you are:
you have product understanding and orientation
you thrive in ambiguity
you turn unstructured reality into actionable models
you are excited when learning something new and applying it
an amazing people-person and team player
you do not shy away from accountability

Required DevOps and Data Engineer
Job Location:
Israel, Hod HaSharon (Hybrid)
Job Brief:
Design, develop, and maintain internal data services, automation flows, CI/CD infrastructure, and database-related tooling.
Job Description:
Design, develop and maintain data pipelines, ETL/ELT flows, and integrations between internal engineering systems
Design and own the DevOps framework for the company and enable the automation infrastructure used by engineering teams
Build automation tools to improve data processing, tooling, and development workflows
Develop and help optimizing the in-house database-related services, including SQL queries, indexing, stored procedures, performance tuning, and monitoring
Ensure data security, permissions, access control, and data integrity across internal systems
Collaborate with cross-functional teams to understand requirements, solve technical issues, and improve internal developer and customer-facing platforms.

we are looking for a Staff Data Scientist.
The Data Science department powers core engine by building production-grade algorithmic solutions at immense scale. As a Staff Data Scientist on the Identity team, you will drive the innovation roadmap for our Identity Graph Engine. You will architect, research, and deploy state-of-the-art machine learning models - spanning graph neural networks, transformers, and deep tabular architectures - to solve complex entity resolution and fraud prevention challenges at scale.
What You'll Be Doing:
Drive Identity Graph Innovation: Architect and advance the next generation of our Identity Graph Engine using state-of-the-art graph models, transformers, and machine learning techniques.
Technical Leadership: Define the research roadmap for identity resolution, setting technical standards and guiding complex research projects from initial concept to high-throughput production systems.
Large-Scale Modeling: Build scalable predictive and representation-learning models over massive tabular, sequence, and graph-structured datasets.
Cross-Functional Architecture: Collaborate closely with Data Engineering, Core Software, and Product teams to seamlessly integrate complex ML pipelines into real-time production environments.
Mentorship & SOTA Research: Benchmark and import cutting-edge academic research into practical business solutions while mentoring senior data scientists on modern modeling paradigms.

We are seeking a visionary and hands-on Staff AI Scientist to help shape the next generation of foundational language models and agentic systems that empower small and medium-sized businesses to thrive. In this role you will define, design, and build groundbreaking AI solutions that understand, reason, and act across complex financial and business contexts.

You will collaborate closely with cross-functional experts across AI research, data science, engineering, and product to create intelligent agents that help SMBs automate financial tasks, make smarter decisions, and operate more efficiently. Your work will combine scientific rigor with visionary execution, balancing theoretical innovation with large-scale practical impact.

As a Staff Scientist you set technical direction beyond your own projects. You are a builder-scientist - a creative thinker who develops novel model architectures and training paradigms and also drives their implementation and validation in real-world applications - and you raise the scientific bar for the teams around you. You will help shape the AI foundation that powers transformative products serving millions of customers.


Responsibilities

Set technical direction. Define the technical strategy and roadmap for next-generation agentic and foundation-model systems, and drive innovation and scientific excellence across the organization.

Build foundational models and agents. Design and develop foundational language models and agentic architectures that power intelligent decision-making for small and medium businesses.

Advance the science. Explore novel approaches in large-scale pretraining, fine-tuning (SFT and RLFT/GRPO), retrieval-augmented generation, and reasoning to improve factuality, adaptability, and alignment.

Solve real financial work. Build intelligent agents that understand financial data, customer context, and business workflows - from bookkeeping and autonomous accounting to planning, communication, and insight generation.

Leverage Intuits data moat. Turn Intuits unique ecosystem signals - counterparty behavior, two-sided transaction visibility, and business twins - into defensible modeling advantages.

Ship end-to-end. Take work from experimentation to production, owning evaluation frameworks and metrics that prove real-world impact.

Elevate others. Mentor scientists and engineers, review technical designs, and grow the craft of the wider team - a core expectation at the Staff level.

Collaborate across functions. Partner with AI research, engineering, product, and design to translate breakthrough science into scalable products used by millions.

Share knowledge. Publish and present research findings, internal innovations, or contributions to open-source frameworks where appropriate.

Required Senior Data Engineer
21679 ID
Role Description:
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.

As a AI-Data Engineer, youll take a leading role in building the foundation of our companys Agentic Exposure Management platform: the source of truth that tells a security team what they have, whats actually risky and why, how to fix it, and who is responsible.
Enterprises run their security across a sprawl of cloud and on-prem tools. Making sense of that sprawl is the heart of what we do. Were building a complex data fabric that reconciles data from a hundred different sources, applies sophisticated transformation and enrichment, and turns partial, uncertain signal into one trustworthy model of an organizations real exposure.
Key Responsibilities:
Model the security graph. Design an ontology that spans both the modern cloud and the classical on-prem world, abstract enough to absorb 100+ different integrations under one model, yet rich enough to carry everything needed for decision making. Decide whats an entity, whats an edge, and whats a denormalized attribute, across millions of assets and finding rows.
Own asset identity engineering and reconciliation. Upstream identifiers are uncertain and volatile. Engineer deterministic identity resolution for assets and findings arriving from different vendors.
Build LLM-based and agentic pipelines for data classification, entity extraction, and enrichment, optimizing for cost, latency, and precision in production.
Build the integration factory. Stand up a scalable integration platform that drives down time-to-integration.
Build and maintain complex ETL pipelines handling millions of records: incremental sync, complex correlation, and data quality observability and monitoring.

We are seeking an experienced Data Science Team Lead to lead our data science and data engineering efforts and oversee a team of skilled data scientists & engineers. This role combines hands-on technical leadership and team management, with responsibilities that include building and scaling data infrastructure to power real-time pricing, large-scale data pipelines, and machine learning products.
The Price Optimization (PO) team is where insights become decisions. We work with large-scale customer data and market predictions - like demand forecasts - to drive revenue management decisions that actually move the needle.
At our core, we build and maintain the decision-making engine. That means designing the data pipelines that bring customer data in, and running it through a price optimization decision model that simulates the market - weighing competition, pricing constraints, inventory availability, predictive models, and each client's unique business policies - to generate the best possible price recommendations. As the final step before recommendations reach the client, quality, reliability, and attention to detail aren't just nice to have. They're everything.
You will lead the team through architecture decisions, development, and deployment of mission-critical systems-while growing and mentoring a high-performing team.
Responsibilities:
Manage a team of data scientists and data engineers responsible for building robust, scalable, and high-performance data pipelines and infrastructure.
Analyzing existing models and algorithms, researching for new approaches in price optimization.
Design, build and maintain distributed data processing workflows (batch & streaming).
Drive best practices for data quality, validation, testing, and observability.
Own and evolve our data architecture in alignment with business and product goals.
Manage sprint planning, task breakdown, code reviews, and performance feedback for your team.
Contribute hands-on to key development tasks and architecture decisions.
Recruit, mentor, and grow the data science engineering team.

The Large Market Model group (LMM) is looking for a Director of Data Science to lead its production engineering, overseeing MLOps, ML engineering and Data engineering. The Director owns the end to end ML lifecycle of the market model, from the data pipelines that feed it, through model training and serving, and on to the monitoring that keeps it healthy once it is live in customer systems.
Crucially, the Director is responsible for setting the roadmap across these functions, alongside LMM management and senior executives, and for turning it into work each function can execute. We are looking for a manager who has led high paced, AI first groups, and who has carried deliveries for systems running live in front of customers.
RESPONSIBILITIES
Decide priorities across the functions with their leads, and review the technical trade-offs yourself.
Manage the leads who run these functions and hold each of them to what their team has committed to.
Stay close to production. Own delivery dates, reliability, and what happens when something breaks.
Own the infrastructure for model training, serving, and large scale data processing, including its cost.
Decide which numbers (KPIs) the group is measured on, then track them and report them.
Report the group's progress and risks to senior management, to product, and to the commercial teams.
Work with the applied research team to move their results into production.
Hire and keep senior engineers, and decide how the group is staffed.
Set the engineering standards the group works to: CI/CD, automated testing, observability, and model monitoring.

We are seeking a skilled and motivated Data Engineer with expertise in Elasticsearch, cloud technologies, and Kafka. As a data engineer, you will be responsible for designing, building and maintaining scalable and efficient data pipelines that will support our organization's data processing needs.
The role will entail:
Design and develop data platforms based on Elasticsearch, Databricks, and Kafka
Build and maintain data pipelines that are efficient, reliable and scalable
Collaborate with cross-functional teams to identify data requirements and design solutions that meet those requirements
Write efficient and optimized code that can handle large volumes of data
Implement data quality checks to ensure accuracy and completeness of the data
Troubleshoot and resolve data pipeline issues in a timely manner.

We are seeking an experienced Technical Team Lead to head the development of a new Cyber Platform. This is a hybrid, 50/50 role, splitting your time evenly between people management and core system architecture & development. You will lead a versatile engineering team composed mostly of Data Engineers, alongside Backend and Frontend developers.
In this role, you will be responsible for a distributed microservices architecture, complex cyber-data fusion, and large-scale data pipelines under strict SLA constraints, operating in both public Cloud and highly secure, closed On-Premises environments.
Responsibilities:
Leadership & Code Quality: Directly lead, mentor, and manage the team while actively conducting Code Reviews (CRs) and setting high engineering standards.
Hands-on Development: Drive core system development (at least 50% hands-on) with a strong focus on Python.
Architecture & Data Pipelines: Lead the design and architecture of distributed systems, high-performance data pipelines, and data-fusion algorithms.
Environment Management: Build and deploy scalable solutions across both Public Cloud and secure, closed On-Prem environments.
Optimization & SLA: Ensure data integrity and optimize complex bulk queries to meet strict response-time targets.

As a Senior Research Scientist, AI & Data Systems at Eon Research, you will conduct applied research at the intersection of AI, data systems, and large-scale storage. you will explore challenging technical problems, publish research in leading peer-reviewed venues, file patents, and translate research breakthroughs into production-ready product capabilities.

This role is ideal for a researcher with a strong publication record, deep expertise in a relevant research area, and experience bringing research into real-world products.

Research Areas
Query Processing and Query Engines: Query optimization and execution over columnar and lakehouse formats, including Apache Iceberg and Parquet, as well as approximate query processing using sketches and synopses.

Storage Systems: Deduplication, compression, and data layout optimization for large-scale storage.

Data Integration and Discovery: Entity resolution, schema matching, table joinability, and data discovery, including the use of LLMs as judges.

Information Retrieval and RAG: Retrieval-augmented generation over enterprise data, including emails, documents, and databases.

LLM Evaluation and Benchmarking: Evaluation frameworks and benchmarks for LLM agents working with enterprise data.

LLMs for Software Engineering: CI failure diagnosis, automated debugging, and program repair.