We are seeking an experienced Solutions Data Engineer who possess both technical depth and strong interpersonal skills to partner with internal and external teams to develop scalable, flexible, and cutting-edge solutions. Solutions Engineers collaborate with operations and business development to help craft solutions to meet customer business problems.
A Solutions Engineer works to balance various aspects of the project, from safety to design. Additionally, a Solutions Engineer researches advanced technology regarding best practices in the field and seek to find cost-effective solutions.
Job Description:
Were looking for a Solutions Engineer with deep experience in Big Data technologies, real-time data pipelines, and scalable infrastructure-someone whos been delivering critical systems under pressure, and knows what it takes to bring complex data architectures to life. This isnt just about checking boxes on tech stacks-its about solving real-world data problems, collaborating with smart people, and building robust, future-proof solutions.
In this role, youll partner closely with engineering, product, and customers to design and deliver high-impact systems that move, transform, and serve data at scale. Youll help customers architect pipelines that are not only performant and cost-efficient but also easy to operate and evolve.
We want someone whos comfortable switching hats between low-level debugging, high-level architecture, and communicating clearly with stakeholders of all technical levels.
Key Responsibilities:
Build distributed data pipelines using technologies like Kafka, Spark (batch & streaming), Python, Trino, Airflow, and S3-compatible data lakes-designed for scale, modularity, and seamless integration across real-time and batch workloads.
Design, deploy, and troubleshoot hybrid cloud/on-prem environments using Terraform, Docker, Kubernetes, and CI/CD automation tools.
Implement event-driven and serverless workflows with precise control over latency, throughput, and fault tolerance trade-offs.
Create technical guides, architecture docs, and demo pipelines to support onboarding, evangelize best practices, and accelerate adoption across engineering, product, and customer-facing teams.
Integrate data validation, observability tools, and governance directly into the pipeline lifecycle.
Own end-to-end platform lifecycle: ingestion → transformation → storage (Parquet/ORC on S3) → compute layer (Trino/Spark).
Benchmark and tune storage backends (S3/NFS/SMB) and compute layers for throughput, latency, and scalability using production datasets.
Work cross-functionally with R&D to push performance limits across interactive, streaming, and ML-ready analytics workloads.
Operate and debug object store-backed data lake infrastructure, enabling schema-on-read access, high-throughput ingestion, advanced searching strategies, and performance tuning for large-scale workloads.

Were looking for a Staff Engineer to lead the design and development of central components within our companys R&D.
Youll combine hands-on development with architectural leadership - solving complex engineering challenges, guiding your team through design decisions, and ensuring our systems are robust, scalable, and maintainable.
Youll collaborate with other senior engineers, tech leads, and product partners to define technical direction, drive innovation, and improve engineering practices across the teams and groups.
This is a role for someone who thrives on building well-designed systems, driving technical excellence within the team and mentoring peers.
Responsibilities
Lead the design and implementation of scalable and reliable backend services.
Drive architectural discussions and make key technical decisions within your domain.
Collaborate with cross-functional teams to translate business and product goals into technical solutions.
Deliver high-quality, maintainable, and testable code while ensuring best practices for security, performance, and reliability.
Mentor and support engineers through design reviews, code reviews, and knowledge sharing.
Continuously improve development processes, observability, and deployment pipelines.
Contribute to the evolution of the platforms architecture as the company and product scale.

We are looking for a Backend Engineer with deep data engineering expertise to build and evolve the systems that power product, analytics, experimentation, and AI development.
You will work across production backend services and data infrastructure, taking technical ownership of the full data lifecycle - from event ingestion and real-time processing to orchestration, modeling, quality, and reliable data access. This role combines hands-on backend development with ownership of shared data platform, working closely with backend, frontend, AI, DevOps, analytics, and product teams.
What you'll do:
Build and maintain production-grade Python and Java backend services, APIs, and reactive data-processing pipelines with clear interfaces, resilient failure handling, and comprehensive observability.
Own and evolve batch and streaming data pipelines supporting product analytics, learner insights, experimentation, and AI model training.
Design and maintain trusted data models, shared metrics, and semantic-layer business logic across the analytical data platform.
Define and enforce standards for data contracts, testing, lineage, freshness, and observability.
Develop reliable approaches to schema evolution, backfills, replay, workload distribution, and failure recovery.
Collaborate with DevOps, backend, frontend, AI, analytics, and product teams to deliver reusable data capabilities and maintain clear system boundaries.

The Networking Diagnostics team is seeking a highly motivated and experienced engineer. This person must have hands-on technical expertise to develop critical diagnostic tools for our newest, world-leading networking platforms. This position offers the opportunity to have a real impact in a dynamic, technology-focused company, impacting data centers and high-performance computing (HPC) clusters across the world. We are looking for independent engineers who excel in a fast-paced environment and are passionate about ensuring the stability of next-generation hardware.

What you'll be doing:
Design & Implement: Architect and develop high-performance diagnostic tools and frameworks using Python, tailored for our latest networking platforms.
Performance Optimization: Drive end-to-end development focusing on software that performs efficiently, ensuring diagnostic suites can handle high-throughput data and real-time monitoring without latency.
System Diagnostics: Take charge of developing features that bridge the gap between complex hardware behaviors and software-level diagnostics.
Collaborative Problem Solving: Partner with hardware, firmware, and driver teams to address real-world challenges that demand innovative solutions and a customer-centric approach.
Autonomous Execution: Own the full software development lifecycle of diagnostic features, from initial hardware specification to deployment in dynamic environments.

We are looking for a Sr. Staff Backend Engineer to join our team. This is a Hybrid (Ramat Gan) role, reporting to the Manager, Software Engineering in the Exposure Management & Security Operations department. Avalor, now part of Zscaler, powers the data foundation behind Security Operations platform, ingesting, unifying, and contextualizing data from hundreds of security and business tools to power security modules that deliver actionable insights and measurable risk reduction to our customers. In this role, you'll own complex backend systems that sit at the heart of our data platform, designing and building services that ingest, process, and move massive amounts of data reliably and at scale for enterprise customers worldwide.

What youll do (Role Expectations)

Developing and maintaining high-quality, scalable backend services within a microservices architecture using Java/Spring Boot and Python
Designing and building data pipelines and ETL workflows that ingest, transform, and unify data from diverse sources at enterprise scale
Leading solutions from scratch to production; creating technical designs, driving architectural decisions and taking full ownership of complex projects - from design and implementation through deployment, monitoring performance, reliability and cost, and proactively driving improvements
Collaborating closely with cross-functional partners - product managers, frontend/backend engineers and UX designers
Serving as a technical anchor for the team - leading design discussions, setting engineering standards, and driving best practices across the codebase
Who You Are (Success Profile)

You thrive in ambiguity. You're comfortable building the path as you walk it. You thrive in a dynamic environment, seeing ambiguity not as a hindrance, but as the raw material to build something meaningful.
You act like an owner. Your passion for the mission fuels your bias for action. You operate with integrity because you genuinely care about the outcome. True ownership involves leveraging dynamic range: the ability to navigate seamlessly between high-level strategy and hands-on execution.
You are a problem-solver. You love running towards the challenges because you are laser-focused on finding the solution, knowing that solving the hard problems delivers the biggest impact.
You are a high-trust collaborator. You are ambitious for the team, not just yourself. You embrace our challenge culture by giving and receiving ongoing feedback-knowing that candor delivered with clarity and respect is the truest form of teamwork and the fastest way to earn trust.
You are a learner. You have a true growth mindset and are obsessed with your own development, actively seeking feedback to become a better partner and a stronger teammate. You love what you do and you do it with purpose.

We are looking for an exceptional, driven, and technically visionary Staff Full-Stack Engineer to help shape the future of our platform and accelerate our mission to simplify taxes for everyone. Youll play a central role in designing and building scalable systems and tools that enable our teams to deliver exceptional user experiences with speed and confidence. If you're passionate about technical leadership, enjoy solving complex architectural challenges, and want to make a meaningful impact on millions of users, wed love to have you join us.

Responsibilities

Critical, cross-functional engineering initiatives from design to development and deployment, ensuring quality and scalability.

Participate in technical decision-making and knowledge sharing across multiple teams.

Collaborate with Product, UX, and other stakeholders to deliver seamless and data-driven experiences.

Evaluate and introduce new tools, technologies, and practices that raise the bar for engineering excellence.

Guide by example - service ownership, observability, and operational best practices across the engineering organization.

Promote a culture of high standards, clean code, testing, and continuous improvement.

Required Software Developer - Production Engineering
Realize your potential by joining the leading performance-driven advertising company!
As a Software Developer on the Production Engineering team in our Infrastructure R&D Department, youll play a vital role in building and scaling the core platform behind one of the largest real-time recommendation systems in the world.
1.4 billion users. 950,000 requests per second. 140TB of fresh data every day. 800M metrics every minute.
Youll join a team of strong engineers operating at this scale, with full ownership from design to production.
Youll have the freedom to move fast, solve complex infrastructure challenges, and help drive what comes next.
How youll make an impact:
As a Production engineering team member, youll bring value by:
Have end to end ownership: Design, develop, deploy, measure and maintain our services
Build and evolve one of the worlds largest real-time systems, including our core framework code, used across R&D
Improve observability, monitoring, and logging to proactively detect and resolve performance issues.
Leverage Gen-AI tools to accelerate development velocity across the R&D organization, enabling further growth of our recommendation engine
Collaborate with the brightest software engineers team members
Directly shape how billions of people discover the internet.
Here are some of the things software devs in our group did in the last several months:
Rewrote part of our recommendation servers to reduce serving time p999 by >40%
Developed internal smart and adaptable cluster management capabilities to allow dynamic resources allocation for heterogeneous traffic patterns
Created and improved our Kafka consumer designed for very high scale
Developed a brand new metrics pipeline, ingesting over 100B metric messages per day
Rolled out a new custom home-built authentication service
Many more things
Our Tech Stack:
Java, Spark, Kafka, Hadoop, Cassandra, Vertica, MySQL, HDFS, BigQuery, Docker, Linux, Kubernetes, Prometheus, Metrictank, Grafana, Airflow, Redis.

In this role, you will architect and implement large-scale distributed systems, low-level platform components, and high-performance services that underpin core product stack. You will drive technical strategy across system reliability, performance, and scalability, partnering closely with product, infrastructure, and data engineering teams to deliver systems that operate at global scale with high availability and efficiency.
Software Engineer, Systems Responsibilities
Architect and implement large-scale distributed systems and platform services that support high-throughput, low-latency workloads across Meta's product infrastructure
Lead the technical design of systems components including storage layers, compute pipelines, networking abstractions, and service orchestration frameworks
Identify and resolve systemic performance bottlenecks through instrumentation, profiling, and targeted optimization across the full systems stack
Define and enforce service level objectives for owned systems, building dashboards, alerting pipelines, and runbooks to reduce mean time to mitigation during incidents
Drive reliability improvements by reducing failure surface, designing resilient rollout strategies, and leading regular resiliency and overload testing exercises
Collaborate with cross-functional partners across product engineering, infrastructure, and data science to align system architecture with evolving product and business requirements
Establish and evolve coding standards, architectural patterns, and engineering best practices for systems development across the broader organization
Leverage AI-assisted development workflows to accelerate design iteration, code generation, and systems analysis, applying sound judgment on when to rely on AI versus deep systems expertise
Mentor other engineers on systems design principles, debugging methodologies, and production operations, and contribute to onboarding programs for new team members
Lead incident retrospectives, identify root causes of complex production failures, and drive implementation of systemic improvements to prevent recurrence

We are at the forefront of the AI revolution, delivering brand new accelerated compute platforms for global impact. Our Network Architecture group is seeking a talented and motivated Sr. Software Engineer to build the agentic workflows that our architects use in their daily work. The software at the center of this role is our hardware network simulation environment - you will design multi-step agent workflows over it, engineer the context that grounds them in our own specifications and source code, and optimize their runtime performance. If you are passionate about building the practical infrastructure that brings intelligent agents to life, we want to hear from you.


What you'll be doing:
Build agentic workflows - loops, graphs, and multi-step pipelines - that carry real hardware network simulation and analysis work end to end.
Engineer the context these workflows run on, turning our simulation models, specifications, design documents, and source code into context that makes agents accurate in our domain.
Work closely with network architects to understand their workflows and translate them into agent workflows they use daily.
Optimize the runtime performance of our simulation tooling on these platforms, including execution time, compute cost, and end-to-end latency.
Define evaluation and regression testing for agent workflows, so that changes to a prompt, a graph, or a context source are measurable.
Build observability across agent runs: what the agent did, where it failed, and why.
Champion guidelines for secure and reliable agent workflows, including data handling, access control, and interaction boundaries.
Serve as a key technical resource for solving sophisticated integration issues between agents and internal tooling.

Were looking for a Senior MLOps Engineer to be a core driver in how our product empowers security teams. You will be expected to deeply understand customer needs and translate them directly into product features that deliver real value. You'll own key parts of our frontend stack, drive key architectural decisions, and turn complex security data into clear, actionable business insights.

As we scale our AIDR product and expand deeper into model-driven security intelligence, we are looking for a Senior MLOps Engineer to own the infrastructure, tooling, and operational foundations that power our NLP and LLM training, evaluation, and deployment workflows.

You will architect and operate the systems that enable us to train, fine-tune, deploy, and monitor models at scale making our ML reliable, fast, cost-efficient, and production-ready.

This is a high-visibility, high-impact role where you will partner closely with DevOps, Backend, Data, and Product to establish world-class ML infrastructure from the ground up.

What Youll Do
Build & Scale ML Pipelines
Design, build, and maintain pipelines for training, fine-tuning, evaluating, and deploying NLP and LLM models across GPU and CPU environments.
Establish LLM-Focused CI/CD
Implement automated CI/CD workflows for ML models, including benchmarking, testing, performance gating, and production deployment.
Optimize Runtime & Inference
Select and optimize serving frameworks for low-latency, high-throughput inference, ensuring reliability and scalability.
Own ML Infrastructure
Manage training environments, experiment tracking, model registries, artifact versioning, and distributed training systems.
Operational Excellence
Monitor and optimize production models for performance, cost efficiency, availability, and observability.