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Help shape how AI data centers are powered. At SolarEdge (NASDAQ: SEDG), we're a global leader in smart energy technology, with over 3,000 employees, offices in more than 20 countries, and millions of installations worldwide. Solaredge is expanding its innovation into the fast-growing world of AI data-center power infrastructure. Building on our leadership in solar, storage, and power electronics, we are creating a next-generation Power Ecosystem — integrating Solid-State Transformers (SST), Battery Energy Storage Systems (BESS as UPS), and advanced power-conversion platforms designed for the AI era. Join our UPS Data Centers group and play a key role in developing SolarEdge's next generation of high-performance power conversion and backup power solutions for data centers. As a Senior Hardware Design Engineer, you will take ownership of hardware development throughout the entire product lifecycle—from concept and design through validation and mass production—working on complex, multidisciplinary systems in a fast-paced environment. What will you be doing?
* Design and develop advanced power electronics hardware for UPS systems.
* Lead hardware development from concept through mass production, including schematic design, board development, validation, and product release.
* Design analog and power electronic circuits for high-power applications.
* Perform hardware bring-up, debugging, validation, and optimization.
* Plan and execute electrical, thermal, EMI/EMC, and reliability tests to ensure product performance and compliance.
* Collaborate closely with Mechanical, Software, System Engineering, Integration, and Manufacturing teams throughout the development lifecycle.
* Support design reviews, root cause analysis, and continuous product improvements.
* Work with laboratory equipment to evaluate, troubleshoot, and validate hardware designs.
* Contribute to system architecture discussions and technical decision-making.

Country:
Israel

City:
Modiin

We are looking for a strong Backend Software Engineer to bridge the gap between our Machine Learning research team and our enterprise production systems. You will act as the technical backbone for our ML Scientists - by advising, designing and implementing the production facing features. If you are a backend expert who wants to solve complex system architecture challenges and dive into the world of ML platforms & Agentic LLM pipelines, this is the role for you - An exciting role collaborating with ML science team, data/infra team and DevOps to drive real customer impact.



As a ML Engineer, you will:



Lead ML delivery: transforming research output (code, models, ideas) into robust, scalable, low-latency microservices in production

Help architect e2e solutions to real customer pains ranging from ingestion, integration, ETLs, DB design up to low-latency services

Design, build, and maintain automated workflows for ML models, including auto-trains, benchmarking, testing, performance gating, and production deployment.

Tackle complex backend challenges: optimizing API response times, managing database connectivity and concurrency at scale, balancing accuracys drive for complex questions with the business needs of fast responsiveness by making hard technical trade-offs between customer gains and business costs.

Design and optimize data pipelines and ETL processes, connecting our Snowflake data warehouse to our training environments.

Work within our existing ML infrastructure (Kubeflow, MLflow, KServe) to ensure smooth model lifecycles and performance monitoring.

Collaborate closely with ML Scientists, guiding them on software engineering best practices without slowing down their research.

Monitor and optimize production models for performance, cost efficiency, availability, and observability.

We're looking for a Senior Support Engineerto be part of the technical backbone of our support organization. You'll join a team of experienced support engineers, bringing together deep technical expertise and strong customer knowledge to resolve the most complex issues our enterprise customers face.

This role is the primary escalation point for our frontline support team - the person they turn to when an issue demands deeper investigation, broader product context, or a more senior technical hand. You'll be expected to lead from the front: diving into complex data pipeline failures, owning resolution end-to-end, creating runbooks, and domain expertise to continuously raise the bar as you always keep your knowledge up to date.

Beyond the technical work, you'll be a trusted face to our enterprise customers - attending customer meetings, communicating with clarity and confidence, and building the kind of credibility that makes customers feel in good hands even when things go wrong.

This is a role for someone who takes real ownership - of the quality of every escalation that moves upstream, and of the knowledge systems that make the entire support organization smarter over time.

What You'll Do:

Know how to take resolution within the SLA end-to-end - ensuring complex issues are resolved thoroughly, not just closed.
Be the technical escalation point from the frontline support team, picking up high-complexity issues and driving them to resolution with minimal unnecessary escalation to R&D.
Own the team's knowledge base and runbook ecosystem - making sure documentation is alive, accurate, and genuinely useful, not a graveyard of stale procedures.
Set and enforce R&D handoff quality standards - every escalation that reaches engineering should be well-investigated, clearly documented, and impossible to bounce back as "not enough info."
Develop deep product area expertise across domains, distributing ownership across your team so that every product surface has a go-to engineer.
Represent the support function in customer meetings - you'll be the technical face of support for enterprise accounts, able to speak credibly about root causes, timelines, and remediation.
Collaborate closely with the Escalation Leader and R&D to close feedback loops, reduce recurring issues, and feed product insights back upstream.

We're looking for a System Architect to own the technical foundation end to end.

What you'll do

Own the architecture - a scalable, cloud-native system from whiteboard to production on a greenfield Azure environment you'll stand up yourself (Kubernetes-native, Everything-as-Code, GitOps, CI/CD from day one).
Work directly with our Chief AI Officer to translate complex decisioning methodologies into clean,self-service products a customer can run without a data-science team.
Lead the coding. Set the standards, make the hard calls, and write the core code yourself. You'll guide the engineers shoulder-to-shoulder - leadership by example, not by org chart, without managing them directly.
Stand up a fully agentic SDLC - building the product withAI coding agents end to end, so a small team ships like a large one.
Design the integration layer so the product plugs into each host's native agent and data surfaces - customers adopt it without leaving the tools they already use.

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 looking for Customer Support Engineerin our site in Netanya with a strong data background, who can provide ongoing support to our client while monitoring our production pipeline ensuring we deliver within the agreed SLA . You will be an essential part of the frontline support team responsible for providing prompt and efficient support and service to end-users experiencing technical issues with software. The primary responsibility is to ensure the timely resolution of data-related problems and to facilitate effective communication between end-users and higher-level support teams. The ideal candidate has relevant experience in data engineering. This role involves coordinating with various teams, managing schedules, and ensuring that production processes run smoothly and efficiently.

Work schedule:

The employee will be required to work 5 days a week, spread across the entire week, including Saturday and Sunday, with off-days during weekdays if shifts fall on weekends. The job is shift-based, with shifts occurring both during the day/evening/night and on weekends.

What You'll Do:

You will be responsible for maintaining and monitoring data pipelines that power our data science workflows while developing and maintaining production schedules.
Identify potential production issues and work proactively to resolve them.
Communicate any production delays or issues to relevant stakeholders and customers.
Maintain accurate records of production activities, including schedules, bugs and project status.
Coordinate with various departments (e.g., Data, DE, PO) to ensure timely completion of tasks.
Serve as the frontline support and provide prompt and efficient support to end-users experiencing technical issues with the platform.
Troubleshoot problems and offer effective solutions to maintain optimal user experiences.
Collaborate with other teams, including R&D and product management, to resolve customer issues.

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.

Responsibilities:

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 transforming our internal infrastructure into a high-velocity, self-service product. This is a senior opportunity to architect the future of our platform, setting the technical standard for how we deliver services while ensuring reliability and developer independence across diverse global markets.

Responsabilities:

Self-service enablement: Build secure, automated pathways that accelerate development while keeping organizational standards intact.
Architectural modularization: Design Kubernetes-native abstractions - controllers and operators - and decouple infrastructure into independent service lifecycles that support rapid iteration.
Everything as code: Treat infrastructure, policy, and configuration with the same SDLC as application software - versioned, reviewed, tested, and CI-gated.
Proactive reliability: Own production through robust observability, clear SLOs, and high operational hygiene; define alerting that fires correctly and lead blameless postmortems that fix the system.

We are looking for Customer Support Engineerin our site in Netanya with a strong data background, who can provide ongoing support to our client while monitoring our production pipeline ensuring we deliver within the agreed SLA . You will be an essential part of the frontline support team responsible for providing prompt and efficient support and service to end-users experiencing technical issues with software. The primary responsibility is to ensure the timely resolution of data-related problems and to facilitate effective communication between end-users and higher-level support teams. The ideal candidate has relevant experience in data engineering. This role involves coordinating with various teams, managing schedules, and ensuring that production processes run smoothly and efficiently.

Work schedule:

The employee will be required to work 5 days a week, spread across the entire week, including Saturday and Sunday, with off-days during weekdays if shifts fall on weekends. The job is shift-based, with shifts occurring both during the day/evening/night and on weekends.

What You'll Do:

You will be responsible for maintaining and monitoring data pipelines that power our data science workflows while developing and maintaining production schedules.
Identify potential production issues and work proactively to resolve them.
Communicate any production delays or issues to relevant stakeholders and customers.
Maintain accurate records of production activities, including schedules, bugs and project status.
Coordinate with various departments (e.g., Data, DE, PO) to ensure timely completion of tasks.
Serve as the frontline support and provide prompt and efficient support to end-users experiencing technical issues with the platform.
Troubleshoot problems and offer effective solutions to maintain optimal user experiences.
Collaborate with other teams, including R&D and product management, to resolve customer issues.

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 AI Full-Stack Developer to build and scale automation solutions across internal company processes using AI, LLMs, and agent-based systems. This role focuses on delivering practical automation solutions, integrating AI capabilities with internal tools, and orchestrating workflows across multiple systems, with a dynamic range of tasks that offer both quick wins and complex challenges.

Responsibilities:

Build AI-driven automation solutions using LLMs, APIs, and agent frameworks.
Develop systems to automate document processing, data extraction, and operational workflows.
Design and implement multi-step automated workflows connecting AI models and internal services.
Integrate automation solutions with internal systems such as HR, finance, legal, dashboards, and ticketing tools.
Deliver and iterate practical automation solutions swiftly, from simple GPT/AI integrations to complex workflows.
Train and support internal teams on newly built or optimized systems for strong adoption and self-sufficiency.
Collaborate with Technical Team Lead, Product Manager, and IT teams on architecture and system integration.
Communicate clearly with internal stakeholders and external partners or vendors.