דרושים » תוכנה » Senior Software Engineer - Platform

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

We're hiring a Senior/Principal Site Reliability Engineer to own production reliability for Cortex Agentix Endpoint Security (following an acquisition of KOI Start Up) as it scales. You'll define and operate our SLOs and error budgets, lead high-severity incident response, and ensure our Kubernetes and AWS infrastructure stays stable under growth. You'll also build and supervise the AI agents that handle routine alert triage and monitor tuning, focusing your own time on the reliability engineering that requires human judgment. This role is a strong fit for someone who treats reliability as an engineering discipline and enjoys ownership, incident command, and applying AI to operational work.
Your Impact:
Own reliability as an engineering discipline - define SLIs, set SLOs, and run error-budget-based decision-making so "how reliable are we" becomes a number that governs how fast we ship.
Own production incidents end-to-end - lead response, mitigation, and resolution for high-severity incidents, and drive blameless postmortems that feed real fixes back into the system.
Own the reliability and capacity of production infrastructure as we scale - forecasting headroom, validating scaling behavior under load, and keeping latency and error rates within SLO.
Run and evolve Kubernetes environments so releases and infra changes are safe by default across hundreds of tenant apps.
Own, build, and supervise our SRE AI agents that triage alerts, review monitors, resolves and summarize incidents. Set and expand the trust ladder that governs what the agents do autonomously, what needs approval, and what stays human. This is a core part of the role.

our company's platform runs inside the security stacks of enterprise customers, processing live detection and telemetry data from SIEM, EDR, and SOAR systems at real scale. Until now, infrastructure has been built and carried by the founding engineering team alongside everything else. You will be the first dedicated owner of it. That means designing our company's cloud foundation across GCP and AWS, building the deployment, scaling, and observability layers from the ground up, and setting the standards the rest of engineering builds on. It also means holding a bar that most startups can defer and our company cannot: we sell to security teams who audit their vendors seriously, so how we run our own infrastructure is part of how we win. This is a hands-on senior role with real architectural authority, and as engineering grows you become the person others learn cloud and infrastructure from.
What you'll be doing
Multi-cloud architecture: Own the design and implementation of our company's multi-cloud architecture across GCP and AWS, including the cross-cloud standards, boundaries, and tradeoffs that keep two providers from becoming twice the complexity.
Infrastructure as code: Build and maintain the entire environment in Terraform, so infrastructure is reviewable, reproducible, and scales without a person in the loop.
Deployment pipelines from scratch: Create the CI/CD foundation that lets a small team ship quickly and safely, with the testing, gating, and rollback paths that make fast releases boring rather than risky.
Kubernetes at data scale: Run and scale the Kubernetes and containerized workloads behind our company's security data processing, where volume, latency, and cost pressure all show up at once.
Security of our own stack: Own identity, access, secrets, network boundaries, and infrastructure hardening to a standard that holds up under customer security review.
Reliability and visibility: Build the logging, monitoring, and alerting layer that tells us something is wrong before a customer does, and make on-call sustainable as the system grows.
Technical direction: Partner directly with the founding engineering team on architectural decisions, and raise the infrastructure and cloud fluency of every engineer who joins after you.

Platform and infrastructure (K8s + IaC)
Architect and operate our Kubernetes platform on AWS - scaling, networking, cost, and reliability
Own infrastructure as code (Terraform) so environments are reproducible, auditable, and fast to evolve
Build the foundations that let the team provision and scale services without friction


AI agent platform - managing and scaling agents at volume
Operate and scale the orchestration layer (Temporal) that runs dozens of agents in parallel
Tune the platform for the unusual load profile of agent workloads - bursty, long-running, data-heavy, latency-sensitive
Give engineers the primitives to deploy, version, observe, and roll back agents safely under enterprise SLAs


CI/CD and developer experience
Own build and deploy pipelines end-to-end - fast, safe, boring releases
Invest in DX as a first-class product: the team treats developer experience as leverage, and you set the bar
Reduce the time from merged to in production and from idea to running experiment


Observability and reliability
Build monitoring, alerting, and tracing that make production legible - for services and for agents
Own incident response and the reliability practices that keep enterprise customers SLAs intact
Turn incidents into systemic fixes, not repeated firefighting


Security and compliance
Own secrets management, hardening, and the day-to-day security posture of the platform
Support our compliance commitments (we are Mastercard-certified, operating in fintech - the bar is high)
Build security into the pipeline so it is the default, not a gate

Were looking for a Senior Infrastructure Engineer who views "Infrastructure as Software." In 2026, we dont just manage servers; we build high-performance environments that allow multi-agent systems to operate at scale.
You will be a core member of the R&D team, blending deep DevOps expertise with the coding rigor of a Backend Engineer. You arent just "configuring" AWS; you are architecting the distributed systems and data pipelines that power our autonomous security brain. Your mission is to ensure that while our agents are evolving and taking actions, our underlying platform remains immutable, observable, and infinitely scalable.
What You'll Do
Design, build, and operate our company's cloud infrastructure using AWS, Kubernetes, and Infrastructure as Code.
Build internal tools and platform services using Python and Go to improve developer productivity and system reliability.
Own infrastructure automation with Terraform, Pulumi, and modern cloud-native tooling.
Partner closely with Backend, Data Science, and Security Engineering teams to build scalable, reliable platforms.
Improve observability, monitoring, and incident response across distributed production systems.
Design and optimize infrastructure for performance, scalability, security, and cost efficiency.
Help shape engineering best practices, platform architecture, and developer experience as our company continues to grow.

LLM optimization is a young field with no settled playbook, so the optimizations you invent here are genuinely new, and they ship into production rather than 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. Then 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. A lot of the job is knowing exactly when an optimization actually wins: deterministic transforms, per-session decision pins, semantic dedup.
Build and own the gateway that runs them. The real-time proxy that carries production LLM traffic, with streaming, retries and 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, whether that's AWS, GCP, Azure, containers or 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 looking for a Senior AI Engineer to design and build production-grade, LLM-powered systems. You'll work at the intersection of software engineering and applied AI - shipping agents, RAG pipelines, and tool-using systems that solve real problems at scale. This is a hands-on, high-ownership role for someone who thrives at the frontier of what's possible with modern LLMs and isn't afraid to write the glue, the infrastructure, and the prompts that make it all work.
This is a **cross-functional, company-wide role**. You won't be embedded in a single product team - instead, you'll partner with every department to identify high-leverage opportunities and build AI-powered tools and workflows that boost productivity and efficiency across the entire organization.
This is a great opportunity to be part of one of the fastest-growing infrastructure companies in history, an organization that is in the center of the hurricane being created by the revolution in artificial intelligence.
"our company's data management vision is the future of the market."- Forbes
we are the data platform company for the AI era. We are building the enterprise software infrastructure to capture, catalog, refine, enrich, and protect massive datasets and make them available for real-time data analysis and AI training and inference. Designed from the ground up to make AI simple to deploy and manage, our company takes the cost and complexity out of deploying enterprise and AI infrastructure across data center, edge, and cloud.
Our success has been built through intense innovation, a customer-first mentality and a team of fearless company ronauts who leverage their skills & experiences to make real market impact. This is an opportunity to be a key contributor at a pivotal time in our companys growth and at a pivotal point in computing history.
What You'll Do:
- Design, build, and operate LLM-powered applications, agents, and workflows end-to-end - from prototype to production.
- Architect retrieval, context engineering, and tool-use strategies that make models reliable, accurate, and cost-efficient.
- Integrate LLMs with internal services, third-party APIs, and data stores to automate complex business and engineering workflows.
- Build, evaluate, and continuously improve evaluation harnesses for non-deterministic systems.
- Collaborate closely with product, research, and platform teams to translate ambiguous problems into shipped capabilities.
- Stay ahead of the rapidly evolving LLM ecosystem (models, frameworks, agentic patterns) and bring the best ideas into our stack.

Zipher is building the Autonomous Execution Layer for cloud data and AI workloads. Backed by $50M in funding , we dynamically orchestrate clusters, predict bottlenecks, and auto-heal infrastructure in real time — with zero human intervention . Our platform runs in production at global enterprise customers, including Fortune 500 companies , delivering mission-critical resilience and sub-second optimization Autonomous execution is a new category, and we are the ones defining it — already running in production inside Fortune 500 environments , where being wrong is not an option. The team is deliberately talent-dense ; every hire changes what we are able to build next. We are looking for a Full Stack Engineer to build our enterprise web application and control plane alongside our senior engineers. You will build the services, APIs, and interfaces that let engineering leaders monitor, understand, and control autonomous data workloads in real time. This is a backend-heavy role . It is best suited for candidates whose primary expertise and passion lie in backend development
What You’ll Do
* Ship features end to end across the control plane: services, APIs, and the interfaces built on top of them
* Build scalable, low-latency APIs over live telemetry from production data and AI workloads
* Build high-performance, real-time visual interfaces and explainability tools for monitoring an autonomous data platform
* Partner closely with backend, AI, and distributed systems engineers to ship complex, mission-critical platform features
* Own quality for what you ship: testing, CI/CD, observability, and the operational bar for a product enterprise customers depend on
What We Offer
* A chance to build the core execution engine for a new category of autonomous data platform — a product already running inside Fortune 500 environments Real ownership from day one : you own what you ship end to end, and you grow into owning whole domains of the control plane Engineering problems that are genuinely hard : real-time telemetry at enterprise scale, low-latency APIs, and interfaces that make autonomous decisions readable A talent-dense team : direct access to the founders and to the engineers who designed the platform, and a bar that pulls you up Top-of-market compensation and meaningful equity Ready to build the control plane for an autonomous data platform? Hit Apply.