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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

In this role, you will operate as a company-level expert in product analytics, partnering with Product, Engineering, and cross-functional leadership to translate complex, large-scale behavioral data into actionable insights that shape the experiences of billions of people worldwide. You will lead the design of rigorous analytical frameworks, advance forecasting and predictive modeling capabilities, and influence the highest-priority product investments through data-driven storytelling and strategic recommendations.
Data Scientist, Product Analytics Responsibilities
Lead the design and execution of complex analytical projects across product areas, applying advanced statistical methods, causal inference, and experimentation to evaluate product impact at scale
Develop and maintain sophisticated predictive models and forecasting frameworks to inform product roadmaps, goal setting, and resource prioritization
Define and evolve success metrics and measurement strategies for product initiatives, ensuring goals accurately reflect user and business value
Identify and size high-impact product opportunities by synthesizing behavioral data, qualitative signals, and business context across multiple data sources
Partner with Product and Engineering teams to design and analyze large-scale A/B tests and feature rollouts, translating results into clear recommendations for product direction
Build scalable, self-service data pipelines and visualization interfaces that enable cross-functional teams to explore and act on product performance data independently
Communicate complex analytical findings to diverse audiences including product leadership and executives through concise, compelling data narratives and presentations
Contribute to functional data science strategy by proactively identifying methodological gaps, championing analytical best practices, and setting goals across teams
Mentor other data scientists on analytical design, hypothesis formulation, and quantitative modeling, elevating the overall quality and rigor of the team's work
Collaborate with Data Engineering and Research partners to improve data infrastructure, eliminate bias in data collection, and ensure analytical outputs accurately represent the populations of interest

We are looking for a Security Researcher to join our team; an enthusiastic, autodidact who is passionate about analyzing vulnerabilities and malware techniques.
He/she will build offensive breach scenarios, integrating the latest security knowledge into the SafeBreach platform of threats and 1-day exploits.

we are looking for a Senior Data Analyst.
The Custom Feeds team turns Placer's data into production-grade bespoke data products for our largest customers - tailored to specific business questions and shipped on a recurring basis. We're looking for a Senior Data Analyst who is both a pipeline builder and a data researcher. You'll own custom recurring feeds end to end - from methodology for new metrics, through pipeline design, to final acceptance before delivery. You'll lead conversations with internal stakeholders (US-based customer-facing teams and colleagues across R&D) and help generalize recurring logic into reusable models, metrics and packages the whole team builds on. Ideal for a rigorous, independent thinker looking for a delivery-oriented role where impact is tangible.
Responsibilities:
Own custom feeds from requirements through delivery and ongoing maintenance
Develop methodologies and acceptance criteria for new metrics
Build reproducible, tested production pipelines in PySpark on Databricks, orchestrated with Airflow and Terraform
Partner with Solution Engineers and customer-facing teams on timelines, methodology and customer feedback
Build reusable components and packages; collaborate with R&D and Data Science to reuse existing datasets, models and products - and make your work reusable by others

we are looking for a Full Stack Developer.
Responsibilities:
Help design, develop, and maintain the front- and backend architecture of our platform.
Participate in end-to-end development, from design through deployment.
Develop new features and infrastructure to support emerging business and project requirements.
Research and integrate advanced technologies to improve system scalability and reliability.
Develop server-side logic and applications.
Ensure application performance, uptime, and scalability, and maintain high standards for code quality and application design.

We are building the first Neuron performance engineering team in Tel Aviv. As a Machine Learning Performance Engineer, you'll help shape the direction of this team from the ground up - profiling and optimizing workloads across the full ML software stack, writing high-performance kernels, and improving the Neuron SDK that external developers depend on. You'll work at the boundary between software and hardware, collaborating directly with compiler, runtime, and chip design engineers to close performance gaps customers care about.

The team is new and small, which means broad scope, direct ownership, and real influence over the technical direction we take. If you enjoy digging into performance bottlenecks and turning analysis into measurable wins, this role is for you.

Key job responsibilities
Design and implement high-performance compute kernels for ML operations, leveraging the Neuron architecture and programming models.
Profile ML workloads end-to-end to identify bottlenecks - memory, compute, or communication - and drive optimizations through to a measured improvement.
Enhance the programming model and tooling that kernel and model developers rely on, improving usability and debugging workflows.
Identify and drive optimization opportunities across the Neuron software stack (compiler, runtime, frameworks).
Document software designs, operational runbooks, and performance findings so the broader team can build on your work.

A day in the life
You might start your morning reviewing profiling data from a customer's large diffusion model training job, tracing a utilization gap back to a specific kernel. After a design discussion with compiler engineers about a new operator fusion strategy, you spend the afternoon writing and benchmarking a kernel prototype. Later, you review a teammate's pull request for a runtime optimization and share your findings in a short write-up for the broader Neuron organization. Your work directly translates into faster model execution and lower cost for AWS customers running ML workloads at scale.

We're hiring a hands-on delivery engineer to take Cortex deployments from initial setup through production rollout and keep them running well afterward. You'll own the technical execution of Cortex projects for our customers: standing up the platform, onboarding data sources and endpoints, tuning detections, and building automation playbooks. Once a deployment goes live, you'll also pick up operational issues for it and for other customers.
Responsibilities:
Configure and deploy Cortex XDR, XSIAM, and XSOAR, translating architecture and design decisions into a working environment
Lead endpoint agent rollout, log source and data onboarding, and integration configuration across a customer's security and IT stack
Tune detection rules and correlation logic to reduce noise and align with each customer's environment and threat profile
Build and maintain XSOAR playbooks that automate response actions and reduce manual triage work
Troubleshoot production issues across the platform: agent health, ingestion failures, integration breakage, playbook errors, and detection gaps
Write clear documentation and runbooks
Escalate to Palo Alto support channels when an issue requires vendor involvement, and manage the case through to resolution
Validate a project's technical scope against the signed SOW before kickoff, and flag gaps or risks early

The Labs Security Team secures the firmware at the foundation of our custom silicon. We work on Graviton processors and the Nitro System - hardware that runs a substantial share of the world's cloud workloads - in close collaboration with the silicon architects and firmware developers who design it. Our engineers operate at the lowest levels of the stack: secure boot chains, hardware root of trust, attestation, cryptographic protocol design, and the boundaries between trust domains within the system.

Key job responsibilities
As a Security Engineer, you will threat model new silicon and firmware architectures, conduct low-level penetration testing against privileged and externally reachable interfaces, review designs and code across multiple firmware components, and build the fuzzing and analysis tooling that makes this work repeatable at silicon scale. You will also help raise the security bar across the wider organization through direct engagement with firmware and hardware teams. We are looking for engineers fluent in C and ARM assembly, with real depth in secure boot, applied cryptography, or embedded exploitation, and the technical credibility to hold a position in a room full of people who designed the system you are testing.

We are seeking a highly skilled and motivated Senior Software Engineer GenAI to join our growing R&D team. In this role, you'll design, develop, and scale our next-generation backend platform, AI-driven workflows, and GenAI applications. You'll work with modern cloud technologies and LLMs to build resilient, high-performance systems while contributing to architecture, technical leadership, and mentoring.

This role is ideal for an experienced backend engineer who enjoys solving complex technical challenges, building scalable systems, and working with cutting-edge AI technologies.

Key Responsibilities

Design and develop scalable backend services using Python.
Build resilient distributed systems, microservices, and AI-driven workflows.
Integrate Large Language Models (LLMs) using AWS Bedrock and other AWS services.
Design and implement event-driven architectures and modern APIs.
Optimize databases, search capabilities, and caching strategies to support high-performance applications.
Contribute to cloud infrastructure, CI/CD pipelines, and observability practices.
Collaborate with Product, Data Science, and Engineering teams to deliver scalable solutions.
Provide technical leadership through architecture discussions, mentoring, and hands-on development.

As a Product Manager at Balance, you will own and evolve core product experiences that power how businesses manage and optimize their financial workflows. You will lead cross-functional initiatives across engineering, design, sales, and customer teams to deliver scalable, reliable, and intuitive solutions. As part of our evolution, you will help shape AI- and agent-driven workflows that redefine how financial operations are built and experienced. You will drive product initiatives end-to-end, identify new opportunities for growth, and ensure alignment with business goals and regulatory requirements.

Responsibilities
Own a core product domain, driving strategy, execution, and outcomes.
Collaborate with cross-functional teams including engineering, design, sales, and marketing to define requirements and deliver high-impact solutions.
Work closely with internal stakeholders and external partners to understand APIs, integrations, and system constraints.
Engage directly with customers and prospects to understand their workflows, validate solutions, and support enterprise conversations.
Translate complex business processes into simple, scalable, and user-friendly product experiences.
Define and ship AI-powered and agent-based product capabilities, both in customer-facing workflows and internal tooling.
Continuously monitor and improve product performance across both technical reliability and key business metrics.
Stay informed about industry trends, regulatory changes, and emerging technologies to identify new opportunities.

Were hiring an AI Backend Engineering Manager to guide and grow a high-impact ML team driving AI-powered innovation across B2B SaaS platform. Youll lead the design and delivery of AI solutions while mentoring engineers and setting the technical direction for AI-first development at scale.
This is a leadership role with a balance of hands-on engineering and team management, perfect for someone who thrives on solving technical challenges, inspiring a team, and shaping the future of AI in fintech automation.
What You Will Do:
Lead & Mentor: Manage, mentor, and grow a team of AI/ML/Backend engineers, fostering technical excellence and career development.
Set Technical Direction: Define the ML strategy, ensuring best practices in architecture, frameworks, and operationalization.
Build and deploy AI-based solutions: Oversee the development and deployment of GenAI/LLM-powered solutions that address real-world challenges across products.
Scale & Operationalize: Establish scalable ML infrastructure, CI/CD, observability, and data pipelines for high-availability production systems.
Collaborate Cross-Functionally: Partner with product managers, engineers, and business stakeholders, clearly communicate progress, challenges, and outcomes.

Were hiring a ML Engineer to accelerate AI-driven innovation across B2B SaaS platform.
Youll be at the forefront of building intelligent systems that power core product experiences and automate internal operations, driving efficiency, speed, and scale across the organization. This is a high-impact, hands-on role in a fast-growing, AI-first company where machine learning is a foundational pillar, not a bolt-on feature. You'll partner with product, engineering, and operations teams to design and implement powerful ML and LLM-based solutions that make a measurable difference.
What You Will Do:
Build Intelligent Systems: Design and develop ML/LLM-powered solutions that solve real-world challenges across product and internal workflows.
Own Full Lifecycles: Take projects from concept all the way to production, including model training, evaluation, integration, and monitoring.
Leverage State-of-the-Art Tools: Work with leading frameworks like LangChain, Hugging Face, TensorFlow, and PyTorch to deliver cutting-edge functionality.
Collaborate Cross-Functionally: Partner with product managers, engineers, and stakeholders to embed AI capabilities into user-facing features and backend services.
Ship at Scale: Build and maintain scalable APIs and services, integrating best practices in CI/CD, observability, and cloud infrastructure.
Report with Impact: Share progress, challenges, and results clearly with technical and executive stakeholders.

We seeking a Senior Python Software Engineer to join our high-impact Risk Team. The Risk Team sits at the heart of our decisioning engines, powering every product through a state-of-the-art fusion of predictive machine learning models and real-time market strategies.

In this role, you will lead backend development for new financial products, working closely with ML Engineers to build robust, fault-tolerant infrastructure that supports complex predictive models. You will design and scale high-volume, low-latency systems that execute critical financial decisions in milliseconds. We are looking for an engineer who understands end-to-end software architecture and possesses the business acumen to transform strategic goals into engineering reality.

Key Responsibilities
Architect & Build: Design and implement resilient, low-latency backend solutions capable of handling high-throughput, high-availability demands.
Solve Complex Systems Challenges: Tackle intricate architectural problems by applying deep expertise in distributed systems and performance optimization.
Cross-Functional Collaboration: Partner closely with ML engineers, product managers, and data scientists to operationalize decisioning logic smoothly.
Maintain & Scale Infrastructure: Safeguard system health, ensure continuous operational stability, and optimize runtime performance.
Drive Technical Excellence: Advocate for clean code, solid design patterns, robust testing, and maintainable software architecture.
Mentor & Empower: Elevate the engineering team by mentoring junior developers, leading technical discussions, and fostering a culture of continuous learning.

We are looking for a Senior Software Engineer to join our team responsible for the machine identity and access offering, designing and developing services used as part of our SaaS products.
The team designs and develops services that integrate closely with leading cloud vendors like AWS, Azure, GCP, and many other DevOps services and tools, providing managed access to sensitive assets in the organization.
In this role, you will be using advanced AWS micro-services, with Ruby, Golang, Java and Python through the entire SDLC from planning to production.
The team collaborates with security architects, product managers, and other development teams.
Together we produce complex and advanced solutions that meet the highest security and scalability standards.
We are looking for someone energetic and versatile to join our family and help us achieve our goals.
Design and develop new features using container-based architectures
Research new technologies and platforms
Be a member of a highly talented cross-functional development team of developers, architects, and QA engineers, in an agile oriented environment
Participate in continuous and iterative engineering cycles, emphasizing code quality, supportability, scalability, and performance.

Were looking for a senior engineer who is ready to move beyond traditional software development and take full ownership of product delivery.

This is not a classic backend engineering role. You will work directly with Product to turn ideas and requirements into production-ready solutions, make the technical decisions, and use AI coding agents as a core part of your engineering workflow.

You will own the outcome, not just the code. AI-generated code is your code, and you are accountable for its quality, security, performance, and maintainability.


Own product features end-to-end, from requirements through production and monitoring.
Work with Product to translate business problems into clear technical solutions and execution plans.
Design scalable and maintainable solutions, including architecture, APIs, data models, and integrations.
Use AI coding agents to accelerate implementation, testing, debugging, and automation.
Review and improve AI-generated code, ensuring high standards for correctness, security, performance, and maintainability.
Make independent technical decisions and communicate architectural trade-offs clearly.
Contribute across backend, frontend, QA, CI/CD, and infrastructure when needed to deliver a feature.
Investigate and solve complex production and performance issues.
Continuously improve how we build, test, and ship software using AI-native development practices.