דרושים » תוכנה » Data Scientist

We are seeking a detail-oriented and collaborative Senior ML Engineer to help support and maintain our machine learning capabilities. This role is ideal for someone who enjoys working closely with production systems, ensuring reliability, scalability, and explainability of models while enabling research teams to deliver impact faster.



Responsibilities



Collaborate with cross-functional teams to ensure ML systems remain robust, explainable, and aligned with business needs.
Monitor and report on ML model performance, reliability, and explainability metrics.
Participate in model retraining procedures, implement automation and optimization of MLOps pipelines.
Extend and scale monitoring pipelines, including support for new features in development.
Investigate, troubleshoot, and resolve issues in production ML workflows (tiered support from initial triage to root-cause analysis with model owners).
Develop and maintain repositories for feature engineering, inference monitoring pipelines, and artifact monitoring tools.
Perform exploratory data analysis (EDA) on historical datasets to identify quality issues and maintain data health.
Implement and oversee production based adjusters across customer deployments.
Evaluate and track critical ML artifacts such as explainability files, coverage metrics, and alignment of features.
Support development and maintenance of internal tools (e.g., interfaces, registries, and feature monitoring frameworks).
Build and maintain static and temporal features, including seasonality, event-based, and price-related features.

We are looking for an MLOps Team Leadto drive the development of an internal machine learning platform for a group of ML teams. This is a hands-on leadership role: you will guide a small team of MLOps engineers that builds the automation and infrastructure powering our research (R&D) workflows and runs our pipelines to production. You will take ownership of end-to-end initiatives and drive the team toward critical infrastructure and model-lifecycle milestones, while staying close enough to the code to set technical direction and raise the bar by example.

You will be responsible for building and maintaining the models and data pipelines behind our data science workflows, ensuring the accuracy, consistency, and efficiency of the data used for training and inference, working across structured and unstructured data from many sources on a large-scale, distributed platform.

WHAT YOU WILL DO

- Lead, mentor, and grow a team of MLOps engineers, owning delivery and technical quality.

- Take end-to-end ownership of infrastructure and pipeline initiatives across the LMM group, from design through production.

- Stay hands-on: contribute to design and code, review work, and set engineering standards.

- Drive the team through critical milestones in ML model-lifecycle and infrastructure ownership.

- Partner with R&D and other stakeholders to translate research needs into robust, scalable systems.

- Help evolve the platform, including our ongoing migration from Dask to Ray.

We are seeking an MLOps Engineer to help us grow our technical team's capabilities. The ideal candidate has relevant experience in data engineering, preferably within the AI field. Aviation industry experience would be a great addition.

You will be responsible for building and maintaining models and data pipelines that power our data science workflows. You'll play a crucial role in ensuring the accuracy, consistency, and efficiency of the data we use for model training and inference. This involves working with both structured and unstructured data from various sources, leveraging your expertise in data engineering and machine learning to create a robust and scalable system.

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 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.

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.
Design, build, and maintain distributed data processing workflows (batch & streaming).
Drive best practices for data quality, validation, testing, and observability.
Own and evolve 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.

Were looking for an experienced Product Analytics Manager to play a key role as part of the Product organization. The Product Analytics Manager will lead the domain and work closely with Product, R&D, Sales and Marketing, Customer Success, and BI to drive data-informed product and business decisions.

Key Responsibilities
Define product success metrics, usage benchmarks, and adoption KPIs across products and customer segments, ensuring alignment with our clinical and business impact goals.
Own the end-to-end product analytics framework - including event design, tracking strategy, data validation, and ongoing data quality monitoring.
Perform hands-on deep-dive analyses, working directly with raw data and collaborating with R&D and BI to ensure robust data models, pipelines, and instrumentation.
Partner with Customer Success and BI to deliver customer-centric insights and support the evolution of the product data infrastructure.
Build dashboards and analytical models to support product and business decision-making, evaluate feature performance, and establish a continuous feedback loop between user behavior and product development - using data-driven insights to challenge assumptions and prioritize high-impact roadmap initiatives.
Quantify the clinical and economic impact of our solutions by analyzing real-world encounter data to validate key performance markers such as reduction in cost of care, improved patient outcomes, and product utilization.
Translate complex analyses into clear, actionable recommendations for technical and non-technical stakeholders.
Provide professional leadership in analytics, including team management (if applicable) and contributing to the growth of the analytics function.

We are seeking a hands-on technical leader to architect and lead the development of production LLM-based applications. This role will lead a cross-functional team of software engineers and data scientists and will own architecture, delivery, evaluation, observability, and production readiness.



The ideal candidate is a proven software leader who has delivered complex production-grade systems and can align engineering, data science, product, and platform work into one cohesive execution model. The role requires strong software architecture, applied AI judgment, disciplined execution, and people leadership, with a focus on turning ambiguous product goals and exploratory AI work into reliable production software.



Responsibilities:

Lead a cross-functional team of engineers and data scientists developing LLM-based applications.
Translate product and business goals into technical roadmaps, milestones, and deliverable plans.
Own end-to-end delivery from architecture and experimentation through deployment, monitoring, evaluation, and continuous improvement.
Design LLM workflows using RAG, tool calling, structured outputs, agents, deterministic logic, and human-in-the-loop patterns where appropriate.
Establish evaluation-driven development practices for LLM features.
Define quality metrics, regression tests, golden datasets, production feedback loops, and observability standards.
Ensure systems meet production standards for scalability, performance, reliability, security, and maintainability.
Provide technical guidance, architectural oversight, mentorship, and career development for team members.
Manage priorities, scope, risks, and trade-offs across multiple streams of work.
Partner with product, design, DevOps, data engineering, security, and other technical leaders.
Drive continuous improvement in delivery quality, engineering practices, and team effectiveness.


Were looking for a technical professional, with proven experience for building secure, scalable artificial intelligence systems as well as data-for-AI solutions. In this role you will develop our cutting-edge AI Data platforms used by world-leading brands.
Responsibilities:
Data Pipelines: Clean, preprocess, and analyze large datasets to ensure high-quality inputs and results.
Deployment & Integration: Help deploy AI agents into production environments and integrate them into existing workflows.
Monitoring & Optimization: Evaluate model performance, test for accuracy, and tweak algorithms for continuous improvement.
Documentation: Maintain clear documentation for AI model architectures, data pipelines, and deployment processes
Scale: Build highly scalable solutions that can leverage large data sets effectively

Required Developer Advocate
This role sits at the intersection of engineering, product, and the global developer community. Youll translate our technical work across all domains, repositories, security, release management, pipelines, distribution, metadata, AI/ML, and ecosystem integrations, into content, demos, talks, and conversations that developers trust. Youll also bring community insights back into our R&D and business teams to influence product direction.
As a Developer Advocate you will:
Represent us across developer, DevOps, security, and platform-engineering communities through conferences, meetups, webinars, podcasts, and online conversations
Create high-quality technical content: deep-dive articles, demos, hands-on guides, screencasts, code samples, and social content
Build compelling demos and storytelling around our core domains: Artifactory, Xray, Curation, Distribution, Pipelines, Workers, Fly, AI/MCP, and ecosystem integrations like GitHub
Collaborate closely with R&D, Product, Security, Solutions Engineering, Architecture, and Customer Success to deeply understand new capabilities and provide practical feedback
Engage with open source and developer communities, finding new channels and conversations where we should have a voice
Partner with Marketing to strengthen messaging while preserving technical credibility
Monitor and report impact across community engagement, content performance, OSS participation, and broader DevRel KPIs.

We are looking for an experienced and leadership-oriented ATE Software team leader to lead our software ATE development team. In this role, you will manage a team of software engineers, overseeing the design and execution of advanced ATE software for electronic cards and calibration systems used in our cutting-edge AESA radar technology The ideal candidate will combine strong software management skills with a deep understanding of RF systems and hardware-software integration. Key Responsibilities:
* Lead, mentor, and manage a team of ATE software engineers.
* Lead the design and development of automated TEST software for electronic cards and radar calibration.
* Oversee the full software development lifecycle, from requirements definition to deployment on the production line.
* Collaborate closely with Hardware, system, and RF engineers to ensure seamless integration of software with complex testing equipment.
* Drive technical excellence, code quality, and the implementation of best practices within the team.
* Manage project timelines, milestones, and resource allocation to meet delivery goals.

The position is based in Netanya, with occasional travel required to the production facility in Beit She'an.

Join GenAI team to build and extend the AI assistant embedded across our product suite. The platform is a production LLM agent system that's integrated into multiple host applications, backed by a RAG knowledge base and an evaluation-driven development workflow.

You'll work end-to-end: agent design, tool implementation, retrieval quality, integration into existing product UIs, cloud infrastructure, and evaluation/observability. The platform already ships to customers - you'll extend it, raise its quality bar, and help define where it goes next.

What you'll do

Design and evolve agents - build LLM agents with tool use, routing, and human-in-the-loop flows.
Implement tools and integrations - expose product capabilities to the agent, with multi-tenant context, via internal APIs and MCP servers.
Own retrieval quality - contribute to our RAG pipeline end-to-end: ingestion, embeddings, vector search, and reranking.
Define and evolve host integration contracts - collaborate with host application teams to integrate the assistant into product UIs built on different frontend stacks. You own the shared remote module and the integration API; host teams own their stacks.
Drive evaluation-led development - write evaluators (rule-based, LLM-as-judge, multi-turn), maintain CI eval gates, and use traces and feedback to debug production behavior.
Operate the platform - own deployments, observability, and the performance and cost of LLM-backed services.
Establish engineering practices** for AI-specific work: prompt versioning, eval coverage, testing, and code review.