לוח משרות דרושים Data Scientist, לחיפוש זה נמצאו 65 משרות

Were looking for a sharp and driven Product Data Scientist to join our fast - paced Data Labs team. As part of a high-performing group of analysts, youll handle complex, custom requests from some of the biggest names in the market, turning raw data into strategic, ad hoc insights. The work is intense and constantly evolving - it requires strong analytical skills and the ability to work independently under pressure. Youll need to be curious, adaptable, and willing to dive deep to find answers where others might stop. If you thrive in a demanding environment and are excited by the opportunity to make a real impact, we want you on our team.
What does the day to day of a Product Data Science Customer Facing (Data Labs) look like:
Building custom-made reports end to end - from meeting with the client and understanding their needs, to writing the code and setting up the report for ongoing delivery.
Apply your expertise in quantitative analysis and data mining to turn data into insights.
Conduct research and develop tools which will help answer client's business. questions by using our raw data sets and algorithms.
Partner with Engineering teams to establish an infrastructure to scale solutions.
Partner with Advisory Services team of Consultants to answer strategic business questions and deliver custom data products seamlessly.

We're looking for a Senior Data Scientist to join the AI cybersecurity team in the Security and Networking Architecture group. As a Senior Data Scientist youll have the opportunity to take an active part in the research and development of our world-class networking and data center security products. This role involves creative problem solving alongside engineering teams, and is key for the continued success of AI networking security.

What youll be doing:

Developing agentic AI systems for security, combining generative models, RAG, and tool-augmented reasoning to automate threat analysis and response workflows.

Optimizing and fine-tuning models for performance, scalability, and resource utilization, considering factors such as latency, efficiency, and cost.

Developing, implementing and improving models and algorithms across media types, whether time series, images, text, audio or video.

Leveraging data pipelines to efficiently process and transform large volumes of data for training and inference purposes.

Applying alignment techniques and parameter efficient fine-tuning to improve model performance.

Measuring and benchmarking model and application performance to drive improvements.

Driving the gathering, building, and annotation of domain specific datasets for benchmarking and training.

Collaborating closely with software and hardware engineers on new features and improvements. Participate in developing and reviewing code, design documents, use case reviews, and test plan reviews.

We are seeking a talented and driven Senior Software Verification Engineer to join our innovative team and tackle SW verification challenges in the domains of high-speed networking and virtualization. You will play a key role in validating and testing complex software products that support the InfiniBand protocol, delivering advanced networking services for cloud, HCP machine learning, and AI workloads.


What Youll Be Doing:

Build automated verification tests in a simulated environment for high-end hardware and software.

Collaborate with multiple teams to understand new network technologies, ensuring accurate modeling of complex network behaviors.

Enhance product quality by improving test coverage and developing comprehensive verification strategies.

Our work in Networking and AI is transforming the world's largest industries and profoundly impacting society. Our Networking product security team is looking for an outstanding technical AI red teamer with hands-on safety and security experience to help us improve the safety posture of AI models, systems and infrastructure. In this role you will reduce risk, threats, and vulnerabilities in our networking AI products.

What you'll be doing:
Drive hands-on safety and security research on a range of AI and networking products.
Develop tools and processes to expose novel weaknesses in AI models and systems to preempt threats.
Participate in defining and ensuring AI development processes meet safety and security standards.
Partner with cross-functional teams to understand needs and implement solutions.
Be a technical focal point across multiple teams and provide hands-on AI safety, security and engineering expertise.

We're hiring an exceptional Applied AI Scientist to join our team!

In this role, you'll design and deploy enterprise-grade AI agents that drive real business impact across Tipalti's core functions - Sales, Marketing, Customer Success, Customer Support, Risk, and Finance. You'll be building agentic systems that execute complex, multi-step business workflows end-to-end. The ideal candidate is a hands-on builder with deep AI expertise who thrives on turning complex business problems into reliable, production-grade AI systems.


In This Role, You Will Be Responsible For


Enterprise AI Agent Development: Designing, building, and deploying AI agents that automate and augment high-value workflows across business functions - including lead qualification and outreach (Sales & Marketing), churn reduction(Customer Success), and Ticket deflection (Support).
Agentic Architecture & Orchestration: Owning the end-to-end agent stack - from tool use, memory management, and multi-step planning to human-in-the-loop escalation patterns, guardrails, and audit trails suited for an enterprise fintech environment.
Knowledge Systems: Building and maintaining RAG pipelines that ground agents in proprietary knowledge - product documentation, customer data, financial records, and internal playbooks - using vector databases, hybrid search, and re-ranking.
Evaluation & Continuous Improvement: Defining agent evaluation frameworks that measure task completion, accuracy, hallucination rates, latency, and business impact - then iterating on agent behavior based on real usage data and stakeholder feedback.
Cross-Functional Collaboration: Partnering with stakeholders across the company to define agentic use cases, translate business requirements into technical features, and deliver products that create measurable ROI - from data preprocessing through product deployment.

High-quality labeled data for REM production programs and our mapping products and tools. We run an operation that deploys labeling for production-scale projects, applies high-standard quality assurance, and then manages and consumes large volumes of data end to end - from annotation through delivery into mapping pipelines.
What will your job look like:
Support ongoing annotation work for REM production programs and mapping deliverables, keeping day-to-day labeling aligned with specs and priorities.
Analyze issues and drive fixes in labels, guidelines, tooling gaps, and vendor output so root causes are addressed
Own and improve quality and efficiency of annotation workflows (QA, rework, throughput) without trading accuracy for speed.
Collaborate across teams - product, mapping, algorithms, operations, and vendors-to align requirements, timelines, and acceptance criteria.
Understand REM technology and real-world map behavior so annotations reflect how roads, lanes, and scene context behave in actual driving.
Work with map and imagery data from many regions (countries, cities, and driving cultures) and keep labeling consistent across geographies.
Run continuous project reviews and produce clear, regular reports on status, risks, quality, and delivery.
Meet project KPIs for label quality, coverage, and schedule
Train and coach offshore annotation engineers on REM concepts, tools, edge cases, and quality expectations.
Interpret complex driving scenes via maps and related data and turn that understanding into practical guidance, specs updates, and scalable solutions for the labeling team.

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.

How will you contribute?
Lead data science projects that directly influence the work of thousands of psychologists, psychiatrists, and other caregivers.
Develop end-to-end AI solutions from the research phase through to production.
Identify and leverage cutting-edge technologies to facilitate their integration into our products.
Mentor and provide technical guidance to other team members.
Play a critical role in designing the companys products.

We are looking for an exceptional Deep Learning Engineer to join our core autonomy team. In this role, you will be responsible for building the AI-driven "brain" of our autonomous driving technology. Your focus will be on designing, training, and deploying state-of-the-art Deep Learning models that handle high-level prediction and driving policy. You will solve complex, real-world challenges by leveraging advanced neural network architectures to optimize decision-making under high uncertainty.
Responsibilities
Model Training & Architecture: Design, train, and optimize cutting-edge Deep Learning models for prediction and driving policy.
End-to-End DL Development: Take ownership of the full DL lifecycle, from data curation and feature engineering to training, evaluation, and deployment.
Scalable Infrastructure: Develop robust pipelines for training and evaluating models on massive, real-world driving datasets.
Production Deployment: Optimize and integrate deep learning models to run efficiently in real-time within the core autonomy software stack.
Full-time availability at our Tel Aviv HQ.

Required Research Scientist - Audiovisual Understanding, Model Foundations
Team & role
The Core Generative AI team is a unified group of researchers and engineers dedicated to developing our generative foundational models that serve LTX Studio, our AI-based video creation platform. Our focus is on creating a controllable, cutting-edge video generative model by merging cutting-edge algorithms with exceptional engineering. This involves enhancing machine learning components within our sophisticated internal training framework, crucial for developing advanced models. We specialize in both research and engineering that enable efficient and scalable training and inference, allowing us to deliver state-of-the-art AI-generated video models.
As a Large Scale Video Understanding Research Scientist, you will play a key role in improving video generation quality and efficiency by improving video and audio understanding pipelines used for both training data construction and model evaluation.. This role demands hands-on work with large-scale Video Language Models (VLLMs), including fine-tuning, post-training, and control, alongside implementing classic computer vision and signal processing algorithms and applying strong research skills. Your expertise in post-training and controlling large scale foundational models, understanding statistics, implementing complex systems and eliminating bugs will be crucial, as our video training sets consist of petabytes of data processed across hundreds to thousands of virtual machines.
What you will be doing:
Fine-tune and control VLLMs for video and audio understanding.
Design algorithms for balancing, filtering, and curating training and evaluation datasets, informed by model behavior and failure modes.
Implement classic and modern algorithms for processing, clustering, evaluation and filtering of large scale datasets.
Work within high-performance, scalable distributed systems capable of handling petabytes of data, with attention to throughput, correctness, and reproducibility..
Collaborate with other researchers and product stakeholders to iteratively improve training sets and evaluation protocols through tight feedback loops driven by model performance.

Required Research Scientist - Audiovisual Generation, Model Foundations
The role
Following the success of LTX-2, our widely adopted open-source text-to-audio+video model, we are expanding our efforts to develop cutting-edge audio+video generation models and are hiring Research Scientists to join our LTX-2 Core team.
Weve been at the forefront of innovation in text-to-image and text-to-audio+video generation, with LTX-2 driving our flagship product, LTX Studio, and gaining significant traction in the research and open-source communities, including integrations with platforms like ComfyUI and Diffusers.
This role focuses on pioneering model architecture and pre-training algorithms, shaping the next generation of our foundational generative AI models.
What you will be doing:
Pre-train and fine-tune video, audio, and image generative models to pursue state-of-the-art results.
Publish papers and open source models to benefit the research community and advance the field.
Design and implement machine learning models for text-to-audio and text-to-video generation.
Collaborate with data engineers to curate and preprocess text and video data.
Optimize models for high performance, ensuring efficient training and inference.
Build new controls and capabilities into generative text-to-audio and text-to-video models.
Stay updated with the latest developments in Generative AI, particularly in the fields of image, video, and audio.
Work closely with product teams to integrate AI models into applications and services.
Conduct experiments and prototype new concepts to advance the capabilities of our AI tools.

Required Research Scientist - LTX Model Evaluation
The Role
As a Research Scientist in Model Evaluation, you are the ultimate authority on model quality and utility. You will design the automated judges, reward models, evaluation datasets, and benchmarking ecosystems that determine the future of LTX. Your mission is to provide the "ground truth" for our pre-training and post-training teams. You will blend the rigor of a researcher with the intuition of a product-thinker, developing metrics that capture both the aesthetic soul of a video and the functional precision required for high-stakes professional use.
Key Responsibilities:
Steer Training & Research: Systematically evaluate model checkpoints to provide actionable insights that guide training experiments and architectural decisions.
Design Benchmark Ecosystems: Develop and run rigorous benchmarks for release candidates against competitive models, ensuring LTX-2 remains world-class.
Build Next-Gen Metrics: Develop robust automatic metrics and Reward Models (e.g., for RL, ITS, auto-research agents) that quantify complex attributes like temporal coherence, physical correctness, spatial accuracy, and foley synchronization.
Diagnose & Analyze: Perform deep root-cause analysis on model failures, providing the diagnostic clarity needed for researchers to implement targeted fixes.
Scale Evaluation: Collaborate with platform engineers to deploy evaluation frameworks across large-scale GPU clusters.

We are seeking a researcher to join the LTX Model Applications team, focusing on advancing and extending the capabilities of our LTX-Video models. You will design and explore new use cases, train novel ways to control the generation process, and push the boundaries of generative video technology. Your work will directly influence millions of users through our apps and contribute to the broader research and open-source communities, shaping the next generation of creative tools.
What you will be doing:
Advance and extend the capabilities of our LTX-Video models through new controls, features, and use cases.
Design and implement machine learning models for video, image, and audio generation, as well as multimodal applications.
Conduct experiments, prototype concepts, and validate ideas to expand generative video technology.
Collaborate with engineers, designers, and product managers to translate research into impactful product features.
Regularly open-source code, models, and research insights, contributing to the academic and open-source communities.
Stay at the forefront of generative AI research in video, image, and audio, and bring relevant advances into our products.
Write high-quality, efficient code and maintain best practices in research workflows.
Mentor other researchers and lead collaborative projects.

The Data Science role is the engine behind our industry-leading Exposure Management (EM) platform.
You will be responsible for the AI models that power our CAASM (Cyber Asset Attack Surface Management) capabilities and our advanced Vulnerability Prioritization and Remediation engines.

We are building the intelligence layer that helps organizations implement a Continuous Threat Exposure Management (CTEM) framework. You will build models that normalize fragmented data from hundreds of sources, calculate complex risk scores, and prioritize which exposures actually pose a threat to the business.

We are looking for an experienced and creative data scientist, with a software development touch, to join our Data Science and our family. A data scientist that understands and loves data, and lots of it.

What you will do...
Solve Applied Product Challenges: Deep-dive into our product ecosystem to identify and solve high-impact problems. You will translate complex customer needs into scalable ML features that directly improve the user experience.
Lead Exposure Intelligence R&D: Develop features that unify asset data (CAASM) with threat intelligence. This includes building models for entity resolution (deduplicating assets across fragmented sources) and automated risk assessment.
Advanced NLP & Knowledge Extraction: Use NLP and LLMs to parse unstructured security data-such as CVEs, threat intel feeds, and security advisories-to automate the mapping of vulnerabilities to specific business contexts.
Predictive Prioritization: Design and optimize algorithms that go beyond static CVSS scores. You will incorporate exploitability (EPSS), reachability, and business criticality to help clients focus on the 1% of exposures that matter most.
End-to-End Ownership: Work closely with Product Managers and Data analysts and Engineers to ensure your models aren't just accurate in a notebook, but are robust, explainable, and deliver clear value within the product UI.
Graph-Based Attack Surface Mapping: Identify hidden patterns and relationships between assets, users, and vulnerabilities to visualize the potential "blast radius" of a security gap.

As a Machine Learning Scientist, you will design, build, and deploy advanced models that guide pricing and promotional optimization across us. You will work closely with other scientists, engineers, analysts, and product teams to translate complex business challenges into scalable, data-driven solutions that deliver measurable impact.

Key Job Responsibilities and Duties:

Develop and deploy models for causal inference, uplift estimation, and optimization to measure and maximize the incremental effect of price and promotion decisions.

Design and improve dynamic pricing algorithms that balance competitiveness, conversion, and profitability.

Contribute to the development of platform capabilities, enhancing experimentation, simulation, and decision-support capabilities.

Partner with product and business stakeholders to translate scientific insights into actionable strategies.

Stay up to date with the latest advances in machine learning, causal modeling, and pricing optimization, and apply them pragmatically at scale.