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This is a broad, hands-on engineering role.

Youll work directly with the founders and own the most challenging parts of the platform end to end.

The focus is backend and infrastructure, but youll work across the full stack whenever a problem calls for it.

Youll help shape the technical direction, and how engineering works as the team grows.

Responsibilities
Own the Challenging Problems - Take the most complex parts of the platform end to end, from architecture through production.
Shape Architectural Decisions - Help define service boundaries, data models, and technical direction as the platform scales.
Build the Core - Design and ship the systems at the heart of the platform.
Scale the Data Pipeline - Build the ingestion, processing, and orchestration layers that move data through the platform reliably.
Own Infrastructure - Shape deployment, observability, and reliability across cloud infrastructure and Kubernetes.
Build End to End - Work across the full stack when the problem calls for it, including the surfaces your services power.

We are a well-funded, early-stage startup looking for a talented and motivated Backend Engineer to join our founding team. The focus of this role is to design, develop, and deploy autonomous AI agents that can automate and optimize complex enterprise workflows. You will work on building intelligent systems capable of decision-making, data extraction, document processing, and other tasks typically requiring human intervention. This is an opportunity to influence the architecture and strategy of AI-driven automation solutions for large-scale enterprise environments.

Your Impact
AI Agent Development

Design and develop autonomous AI agents using best of breed large language models to automate tasks such as document processing, data extraction, and workflow management.

Implement reinforcement learning techniques to enhance decision-making capabilities of AI agents.

AI Integration

Build and integrate APIs that connect AI agents with external systems and enterprise software (ERP, CRM).

Use frameworks like TensorFlow, PyTorch, or Hugging Face to deploy and optimize AI models for real-time processing.

Data Processing and Pipelines

Design and manage data pipelines to process and analyze large volumes of documents and unstructured data efficiently.

Optimize data handling to improve speed and accuracy of AI agents.

Security and Compliance

Implement authentication and authorization mechanisms to secure AI-driven systems.

Ensure compliance with data privacy standards (e.g., GDPR, HIPAA) and adopt best practices for secure data handling.

Cloud Infrastructure and Scalability

Deploy AI agents on cloud platforms (AWS, GCP, or Azure) ensuring scalability and reliability.

Leverage containerization (Docker, Kubernetes) for efficient deployment and management.

Testing and Optimization

Develop and execute unit, integration, and performance tests for AI-driven systems.

Continuously monitor and optimize system performance for speed, accuracy, and cost-efficiency.

Collaboration

Work closely with AI researchers, front end engineers, and product teams to align AI agent capabilities with business requirements.

Participate in code reviews, design discussions, and architecture planning to drive innovation.

we are looking for a Senior Software Engineer.
As a Senior Software Engineer, you will be responsible for designing and building core backend services and product capabilities, working on scalable systems that handle large volumes of security-related data, and developing robust features that improve security posture in cloud environments.
The ideal candidate will have strong software engineering foundations, a product-minded approach, and experience building distributed systems in production.
You will collaborate closely with Product, Research, and Engineering teams to deliver secure, performant, and reliable solutions end-to-end.
You will work within the engineering department, contributing to architecture decisions, improving code quality, scalability, and reliability, while maintaining strong ownership over development and delivery in a fast-paced environment.

Our Tel Aviv Research and Innovation Center, is looking for an experienced software engineer with expertise in Linux kernel and virtualization design + development to join our Future-Computing-Infrastructure expert group. The group designs and develops technologies for the next-generation data center aimed at accelerating AI workloads for a unique HW, optimizing compute resource utilization and reducing data-center costs. Our projects involve hardware and software architecture co-design. They require high-level system understanding, creativity and innovative thinking.
If you want to be part of something bigger, if you are a team player with excellent communication skills and motivation to revolutionize data-center technology, youre welcome on board!


What will you be doing?
Design and develop Linux kernel code related to virtual memory management and memory-tiering.
Research, design and innovate methods, algorithms and supporting data-structures for managing memory hierarchies in virtualization environments
Participate in research to analyze AI-based applications performance, identify bottlenecks and optimize various decision-making mechanisms based on benchmarking results.
Collaborate with professional team members and various global teams around the world on a daily basis.
Challenged to think outside of the box and come up with creative ideas to deal with complex challenges within Linux kernel.

We build software products for clients across embedded, IoT, SaaS, AI/ML, e-commerce, mobile, and web. Were looking for a Senior .NET Backend Engineer to join a strong, high-bar team building high-performance, secure .NET services. Youll be a hands-on contributor - designing and writing production backend code, shaping technical decisions alongside your teammates, and delivering for clients (including large enterprise and banking environments).
What youll do:
Design and build robust, high-performance, secure backend services in C# / .NET.
Contribute to backend architecture and technical decisions alongside the team.
Design and document APIs (Swagger / OpenAPI) for clean, well-versioned integrations.
Take part in code reviews and help uphold the teams engineering standards.
Drive performance, reliability, and secure-by-design practices in your code.
Contribute to build, packaging, and deployment so releases are repeatable and reliable.

We're backed by tier-1 global VCs, led by second-time founders, and moving fast because the problem demands it. There are no silos here. No "that's not my area." If it ships, you own it.
We're hiring a full-stack software engineer who wants end-to-end ownership - backend, frontend, infrastructure touchpoints, and AI-driven workflows.
If you're looking for narrow scope or strict boundaries, this is not the role. If you like building real systems and taking responsibility for outcomes, read on.
What you'll do:
Own systems end-to-end - infrastructure, pipelines, and their behavior in production
Run and evolve our cloud infrastructure - Kubernetes, Kafka, Terraform, GitOps (AWS today, multi-cloud ahead)
Build the data backbone - ingestion, streaming, and pipelines into our lakehouse that stay correct and affordable as volume grows
Build AI-native engineering workflows: Sandboxes and dev environments, Automated verification and guardrails, Feedback loops that keep quality up as output scales.
Build golden paths - repo to production without a human bottleneck
Use Claude / Codex / AI copilots as default tools - not optional helpers
Debug production issues across the stack (no handoffs)
Ship fast, learn fast, fix fast
Take responsibility for what you build - including after it's live

We're backed by tier-1 global VCs, led by second-time founders, and moving fast because the problem demands it. There are no silos here. No "that's not my area." If it ships, you own it.
We're hiring a full-stack software engineer who wants end-to-end ownership - backend, frontend, infrastructure touchpoints, and AI-driven workflows.
If you're looking for narrow scope or strict boundaries, this is not the role. If you like building real systems and taking responsibility for outcomes, read on.
What you'll do:
Own features end-to-end - backend, frontend, APIs, and behavior in production
Build AI-native workflows:
Agent-driven actions
Human-in-the-loop interfaces
Explainability and control surfaces
Use Claude / Codex / AI copilots as default tools - not optional helpers
Work directly with product and customers to refine behavior
Debug production issues across the stack (no handoffs)
Ship fast, learn fast, fix fast
Take responsibility for what you build - including after it's live

he industry leader in Autonomous Security Operations, is hiring a Senior Software Engineer to help build the platform that's redefining how cyber-security teams operate. This is a great opportunity for
experienced engineers who want to work on high-scale distributed systems and AI-driven workflows at a top-tier AI-first engineering company. The ideal candidate is a strong backend engineer who thrives on scale, performance, and cost challenges, and is excited to own features end-to-end.
Responsibilities:
Develop a best-in-class AI SOC platform that automates SOC tasks using AI, automated forensics, threat intelligence, and comprehensive analysis
Build and integrate AI workflows in a big data system that processes massive, continuously growing volumes of data daily
Own the full feature cycle, from research and design through to deployment and production monitoring
Tackle scale, performance, and cost challenges at every layer of the stack
Join a team of talented engineers who strive to make a huge impact on our users and product

We're forming a new AI Group and looking for a Senior AI Engineer to help shape it from an early stage - a greenfield, long-term effort to evolve how decisions are made across the platform using AI-driven systems. You won't just integrate APIs or build demos; you'll build the AI brain that works alongside (and increasingly drives) our core automation engine, with real production impact from day one and room to grow into technical leadership as the group scales.

Agentic AI Architecture: Design and build autonomous AI agents that analyze infrastructure in real time and make intelligent decisions. Work with modern agentic frameworks (LangGraph, PydanticAI) and conversational AI to create multi-agent systems - including troubleshooting, optimization, FinOps, and how-to agents. Leverage core LLM capabilities (tool-use, memory, retrieval) to operate safely in production.
Platform Integration & Intelligent Decision Systems: Develop MCPs to expose capabilities to AI agents that reason over infrastructure environments, metrics, configurations, and cost signals. Build integrations with tools like Slack, Jira, and AI-powered IDEs (Cursor, Windsurf) to deliver context-aware insights, from "why is this pod not scheduling?" to "how can we reduce costs by 30% safely?"
AI Model Development & MLOps: Build and deploy machine learning models that learn from infrastructure patterns - detecting the right resource policies for workloads, predicting optimal scaling triggers, and recommending GPU configurations. Own the complete ML pipeline from training to production, ensuring models are reliable, monitored, and continuously improving.
R&D AI Tools Development & Adoption: Build and embed internal AI tools to accelerate engineering, development, research, and support.
AI Tools for Business Impact: Develop AI-powered tools that help Sales and Support teams demonstrate value instantly - agents that analyze customer infrastructure, generate cost optimization reports automatically, and turn technical data into clear business recommendations.
End-to-End Ownership: Own AI systems from concept to production, ensuring they're fast (sub-2-second responses), reliable, safe, and cost-effective. Build evaluation frameworks to measure quality, implement security controls, and balance performance tradeoffs in production.
Technical Leadership: Define AI architecture and best practices as a founding member of the AI team. Make key technical decisions - choosing frameworks, designing multi-agent systems, establishing data governance - and shape how evolves from AI-enhanced internal tools to customer-facing AI products.

We are seeking a hands-on Tech Lead to join our Backend Engineering team and lead the shaping of the architecture and development of our Kubernetes-based platform. This role combines strong technical ownership with team leadership and is ideal for someone passionate about building complex systems and mentoring others. As a Tech Lead , you'll be a technical authority in everything cloud-native - leading feature development, making key architectural decisions, and collaborating with cross-functional teams to drive innovation.

Lead backend development efforts and serve as a key decision-maker on architecture, design, and implementation
Work closely with other engineers to develop robust, scalable, and efficient systems
Dive deep into Kubernetes, Go, microservices, and other backend technologies
Take technical ownership while mentoring engineers and promoting best practices
Collaborate with product and DevOps teams to ensure smooth feature delivery
Act as a thought leader and help shape ScaleOps' technology vision

LLM optimization is a young field with no settled playbook, so the optimizations you invent here are genuinely new - and they ship into production, not a paper. And they matter: real enterprises run enormous volumes of LLM traffic and feel every wasted token, so the work you do lands on live customer requests and solves a problem they actually have. It's about as close to the frontier, and as close to the product, as engineering gets.

03What you'll do
Invent and ship optimizations. Design new strategies across every layer of a request - prompt and context compression, tool pruning, file and dev-command trimming, semantic caching, cache-prefix stabilization - and take them all the way to the hot path. Each one has to cut tokens on real traffic without degrading output.
Prove every win. Build the evals and the token/quality instrumentation that separate a genuine saving from a plausible-sounding one. An optimization only ships when the numbers back it.
Live in the caching trade-offs. Provider prompt caches only pay off on byte-stable prefixes, and a naive rewrite can cost more than it saves. Knowing exactly when an optimization actually wins - deterministic transforms, per-session decision pins, semantic dedup - is a lot of the job.
Build and own the gateway that runs them. The real-time proxy that carries production LLM traffic - streaming, retries, backpressure - executing every optimization on the live request path.
Keep it fast and deploy anywhere. Defend a tight latency budget on the hot path, and package the whole thing so it installs cleanly into any customer's cloud - AWS, GCP, Azure, containers, Kubernetes - and upgrades without drama.
Own systems end to end. From first commit to code serving live customer traffic - plus the spend-visibility and governance product built on top - with a direct line to the founders.

We are seeking an experienced and highly motivated Senior Software Engineer to join our innovative engineering team. As a Senior Engineer, you will play a key role in designing, building, and optimizing our DataOps platform. This is a hands-on role where youll be solving complex challenges and driving innovation in how data operations are managed. Youll collaborate with cross-functional teams, helping to bring new features to life while ensuring the performance and scalability of our platform. This is an incredible opportunity to make a meaningful impact on the future.

What You'll Do
Design & Develop: Design, develop, and maintain scalable and reliable code for DataOps platform, ensuring high performance, security, and scalability.

Lead Features: Take ownership in leading the development of core features and services from conception to completion, with minimal oversight.

Mentor & Lead: Mentor junior engineers, sharing knowledge and best practices, and provide technical leadership across projects.

Drive Improvement: Champion continuous improvement efforts to ensure our systems are adaptable to evolving business needs.

Troubleshoot & Optimize: Troubleshoot and resolve complex technical issues in both development and production environments.

Collaborate: Work effectively with cross-functional teams (Product, Design, etc.) to translate requirements into robust technical solutions.

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.

We're looking for a Senior Engineering Manager to lead our Data Platform group - owning the pipelines, storage, and streaming infrastructure, deployed on AWS, that power data-driven capabilities across the company, including an emerging AI agent for forensic data investigation.

Key Responsibilities:

Own end-to-end delivery for the group - roadmap, execution, and Agile/SAFe process.

Hire, mentor, and grow a team of engineers.

Guide architecture and technical direction for scalable data pipelines (real-time and batch), streaming (Kafka), and processing frameworks (Spark/Flink).

Oversee the platform's AWS infrastructure and cloud architecture decisions.

Ensure sound data storage design across relational, NoSQL, and search systems.

Drive improvements in reliability, observability, and cost efficiency.

Guide the team's development of an AI agent for forensic use cases, including MCP tool design for safe, effective data access.

Represent the group across a matrixed org and partner with product teams.

We're looking for a Platform Tech Lead to join our Platform Engineering team.
In this role, you'll help shape the platform that powers all of our company's R&D teams. You'll lead technical decisions, design scalable cloud infrastructure, build high-performance services, and drive engineering excellence across the organization. This is a highly hands-on role for someone who enjoys solving complex technical challenges while influencing architecture, scalability, and developer experience.
What You'll Do
Design, build, and maintain scalable backend services using C# and .NET Core.
Lead the design and evolution of our cloud-native platform and microservices architecture.
Build infrastructure and developer platforms that enable engineering teams to move faster.
Work with Kubernetes, Docker, and cloud platforms (Azure, AWS, or GCP).
Drive architectural decisions with a strong focus on scalability, reliability, security, and performance.
Collaborate closely with engineering teams to improve developer experience and platform capabilities.
Mentor engineers and serve as a technical leader across multiple teams.