we are looking for a Solution Engineer.
Responsibilities:
Lead and support customer-facing Demand Forecasting use cases from discovery through deployment
Work hands-on with customer data to design, test, and validate forecasting solutions
Act as a trusted analytical and technical advisor to customers, deeply understanding their business context and datasets
Analyze and explain model outputs and complex analytical concepts in a clear, business-oriented way
Troubleshoot technical and data-related challenges and drive them to resolution
Collaborate closely with Data Science and Customer Success teams, providing structured feedback from customer use cases
Help customers achieve measurable business impact and adoption of Demand Forecasting module

we are looking for a Customer-Facing Data Scientist.
As a key Customer-Facing Data Scientist on the Demand Forecasting team, you will:
Work at the forefront of the data world, focusing on the optimization and deployment of Demand Forecasting models.
Handle complex data science challenges using state-of-the-art techniques relevant to demand forecasting and time series, such as forecasting model ensembling, anomaly detection, feature engineering for trend/seasonality, and causal inference modeling.
Act as a highly communicative partner to our clients, diving deep into their unique datasets to understand the nuances and challenges of their business.
Lead the rigorous testing and application of our core ML pipeline on new customer data, identifying areas where it performs exceptionally well and researching/developing solutions for complex cases where it does not.
Be instrumental in helping our clients achieve their business goals and realize the measurable value of Demand Forecasting module.
Work within a production and product-oriented environment, ensuring models are robust, scalable, and directly integrated into the clients operational workflow.

we are looking for a Predictive Analytics Solution Engineer.
Typical Tasks:
Guide and assist customers in utilizing the platform and implementing AI models.
-Help customers understand complex AI concepts, identify relevant use cases, and implement solutions to drive business value.
Address and resolve technical and data-related issues.
Provide valuable feedback to the product team and collaborate to enhance the product.
Analyze model outputs and explain complex ideas in a clear, understandable manner.
Serve as the primary data and technical contact for clients, understanding and addressing their needs.

We are looking for hard-working, sharp, and humble professionals with proven technical customer-facing experience. Our architects are trusted advisors and consult our customers upon their monitoring, security & observability journey.

Solution Architect is a senior-level role expected to professionally and accurately present technical presentations across different personas, from software engineers and DevOps all the way to the C-suite.

What youll do:

Collaborate with account executives to strategize, prepare, and execute on complex, technical sales cycles.
Deliver compelling presentations and product demonstrations that highlight our unique value proposition and the solutions we provide.
Oversee the technical aspects of the sales process, including sizing, discovering and Proof of Concepts.
⁠Develop custom solutions to bridge any gaps and enhance the core offerings of our product.
⁠Possess a thorough understanding of the Observability market, advising customers on best practices and the most appropriate solutions for their needs.

We are looking for an exceptionally strong software engineer to join our Technology organization. You will be part of a business-facing team responsible for building and evolving the core systems that power our Quant strategies. This is an AI-forward team: large language models (LLMs) and AI agents are part of our daily workflow - from writing and reviewing code, to automating operational toil, to building internal copilots and agentic tools that the whole team relies on. You won't just use AI to move faster; you'll help shape how it's applied across our engineering practice.
Design and build data pipelines and the data layer.
Develop and maintain the computational frameworks and infrastructure that run our quantitative strategies at scale.
Implement and enhance quant-related software systems for data, research, portfolio construction, and trading.
Work closely with Researchers, and Quants as a hands-on engineering partner, helping them turn ideas into robust production solutions.
Gain exposure to the full lifecycle of the systematic/quant business, from data ingestion through research, implementation.
Build and integrate AI-powered tooling - LLM agents, copilots, and automation - into engineering and research workflows.
Use AI coding assistants and agentic developer tools to ship higher-quality software faster.

As a Senior Big Data Engineer, working within Mobility Group, you will play a pivotal role in designing, developing, and maintaining the data infrastructure that powers our location analytics platform.
RESPONSIBILITIES:

Data Pipeline Architecture and Development: Design, build, and optimize robust and scalable data pipelines to process, transform, and integrate large volumes of data from various sources into our analytics platform.
Data Quality Assurance: Implement data validation, cleansing, and enrichment techniques to ensure high-quality and consistent data across the platform.
Performance Optimization: Identify performance bottlenecks and optimize data processing and storage mechanisms to enhance overall system performance and reduce latency.
Cloud Infrastructure: Work extensively with cloud-based technologies (GCP and AWS), to design and manage scalable data infrastructure.
Collaboration: Collaborate with cross-functional teams including Data Analysts, Data Scientists, Product Managers, and Software Engineers to understand requirements and deliver solutions that meet business needs.
Data Governance: Implement and enforce data governance practices, ensuring compliance with relevant regulations and best practices related to data privacy and security.
Monitoring and Maintenance: Monitor the health and performance of data pipelines, troubleshoot issues, and ensure high availability of data infrastructure.
Mentorship: Provide technical guidance and mentorship to junior data engineers, fostering a culture of learning and growth within the team.

Were seeking a Solutions Engineer who thrives in fast-paced, customer-facing environments and enjoys building and shipping complex enterprise integrations, solving production challenges, and turning scoped technical projects into reliable, scalable solutions.
As our customers turn more enterprise and more global, our delivery org is where deals become live production systems. As a Solutions Engineer, you're the person who makes integrations real - you take a scoped project and ship it: fast, high-quality, and on the timeline you committed to.
You'll build and test the two-way integrations that connect our company's agents to our customers' enterprise systems, debug issues in production, and clearly communicate impact and plans back to the team and the customer. You're a hands-on integrator at heart, but you can hold your own in a room - explaining what you built, why it behaves the way it does, and what happens next to both engineers and business stakeholders on the customer side.
You'll work inside a delivery pod alongside Technical Project Managers, Solutions Architects, and your Solutions Engineer Manager, on both presale POCs and long-running production deployments.
* This is a Tel-Aviv based role with some travel to the USA office and to global customers expected.
What You'll Do
Implement customer integrations quickly and at sufficient quality - from POC scope through production
Match committed timelines, and surface risks and rejections early where relevant
Debug production issues and communicate impact & remediation plans effectively
Participate in POC and production implementation work alongside customer ICs, and lead technical working sessions with them
Build integrations that hold up under real enterprise load - queues, retries, rate limits, race conditions
Contribute to reproducible playbooks so each integration is faster and more predictable than the last.

We are a fast-growing cybersecurity startup redefining how organizations protect themselves. Our Data Team is at the heart of that mission - powering everything from security insights to customer-facing intelligence. We are looking for a Software Engineer to help design and develop the core services and applications that drive our data capabilities. In this role, you will build high-quality, scalable software; contribute to the architecture of our next-generation data systems; and collaborate with research and product teams to turn cutting-edge ideas into production-ready features.
Responsibilities:
Design, develop, and maintain scalable backend services and components that power our data workflows, analytics, and product features.
Build high-quality internal tools and applications that improve engineering workflows and enable data-driven development across the organization.
Contribute to the architecture and evolution of our new data platform with a strong focus on clean design, testability, maintainability, and performance.
Collaborate closely with security researchers, analysts, and product managers to translate innovative cybersecurity concepts into reliable, production-ready software.
Apply engineering best practices across the stack - including code quality, testing, observability, versioning, and documentation - to ensure system robustness as we scale.

we are looking for a MLOps Team Lead.
As an MLOps Team Leader , you will own our AI infrastructure and backend engineering efforts, leading a team at the intersection of systems design and machine learning to build the engineering systems that turn Deep Learning and Computer Vision research into reliable, scalable production features.
What Youll Be Doing:
Lead MLOps Engineering: Own the roadmap and execution for a team of backend and AI infrastructure/MLOps engineers, setting technical direction, removing blockers, and holding the bar on delivery quality.
Build Production AI Systems: Design and implement production-grade, end-to-end AI solutions, including agentic workflows, that integrate Deep Learning models and Computer Vision algorithms into real features. Own the offline experimentation and validation layer, including POC environments that let the Algo team move from research to production confidently.
Architect Data Platforms: Drive the design and implementation of scalable data platforms and pipelines that power AI capabilities reliably and with an eye on cost and scale.
Cross-functional Collaboration: Work closely with the Algorithms and Data teams to tackle complex, real-world problems, translating research into shippable, maintainable systems.
Elevate Engineering Standards: Champion Software Engineering and System Design best practices across the group, introducing the right methodologies, tooling, and culture of craft.
Who You Are:

We are looking for a Staff Architect, Data & AI Infra to shape, build, and scale the infrastructure that powers our data, AI, and research platforms. This is a senior player-coach role with broad architectural ownership across data infrastructure, ML infrastructure, developer experience, reproducibility, and production reliability. You will work across the wider engineering group as a hands-on technical architect, while also managing a small team of individual contributors focused on ML infrastructure.

This role is ideal for someone who can move between long-term platform architecture and practical execution: defining standards, building core systems, mentoring engineers, improving reliability, and partnering with Data Engineering, AI/Research, Product Engineering, Security, Bioinformatics, and Leadership to make our data and AI platforms scalable, reproducible, secure, compliant, and easier to use.

Location: Ramat Gan, Israel (hybrid model)

What will you do?

Architectural Leadership: Own and evolve the technical roadmap for our data and AI platforms, ensuring scalable and reliable architecture that supports current needs and prepares for a multi-cloud future.
MLOps & Platform Development: Design and build end-to-end MLOps systems-covering experimentation, training, reproducibility, and deployment-while managing specialized infrastructure like BigQuery, orchestration tools (Dagster/Airflow), and R/Python workloads.
Infrastructure Strategy: Define and lead strategy for GPU resources (scheduling, utilization, batch compute) and establish engineering best practices, data architecture standards, and platform guardrails.
Developer Experience: Enhance developer productivity by building self-service platforms, automation, internal tooling, and reusable templates that simplify workflows and reduce operational friction.
Team Leadership: Act as a player-coach to mentor engineers and manage a small team of ICs, fostering a culture of sound decision-making and technical excellence across the broader group.
Security & Reliability: Partner with Security to enforce compliance (SOC2, HIPAA, GDPR) and access controls, while mitigating operational risk through improved observability, incident readiness, and robust support processes.