דרושים » תוכנה » Infrastructure Tech Lead

Required Software Engineer III, Platform Engineering and Infrastructure
About the job
Our software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to our needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
As a part of this team, you will join the premier engineering powerhouse driving Chronicle Security Orchestration, Automation and Response (SOAR) to operate at true scale. As hyper-focused experts in Platform Engineering and Infrastructure, the team holds absolute ownership over the Security Operation provisioning service, orchestrating complex resources in Cloud Platform and mission-critical workloads in Kubernetes (GKE) and in Compute infrastructure with precision. You do not simply respond to operational issues, but build the platforms, tools, and developer infrastructure that transform complex issues into software-driven solutions.
Responsibilities
Write product or system development code.
Participate in, or lead design reviews with peers and stakeholders to decide amongst available technologies.
Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.

We are looking for a highly driven and experienced AI Engineer Team Leader to manage and mentor a core team of engineers while remaining 50% hands-on. In this critical role, you will lead the development of our enterprise Agentic AI platform, blending deep technical expertise in AI agent development with strong engineering leadership.
As a Team Leader, you will drive the technical agenda, plan sprints, and collaborate closely with Product Managers and cross-functional partners to turn powerful capabilities into reliable, safe, and fast product experiences. You must thrive in a high-pressure, dynamic startup environment characterized by a fast pace and frequent pivots, ensuring your team remains focused, agile, and aligned with business goals while executing at the highest level.
You won't just manage - you'll own agent workflows end to end, from design through evaluation, deployment into the tools employees use daily, and iteration in front of enterprise customers.
Responsibilities
Team Leadership & Management (50%): Manage, mentor, and grow a team of AI and software engineers. Oversee task allocation, sprint planning, and daily agile ceremonies.
Hands-On Engineering (50%): Actively code, design, and build multi-agent systems and orchestration - intent routing, planning, tool use, and coordination across specialized agents.
Technical Strategy & Architecture: Drive the technical agenda, architecture decisions, and best practices for developing scalable, LLM-driven applications grounded in our knowledge graph.
System Integration: Integrate LLMs with tool/function calling and protocols such as MCP to give agents controlled access to HCM systems, business logic, and workflows.
Production Excellence: Build evaluation harnesses, guardrails, and safety/bias checks. Optimize agents for latency, cost, and reliability at enterprise scale. Take agents from prototype to production - with monitoring, observability, and a fast iteration loop.
Cross-Functional Collaboration: Work intimately with Product Management, Data, and Design teams to translate customer needs into agent capabilities, define roadmaps, and prioritize tasks.
Agility & Execution: Lead the team through frequent pivots and rapid development cycles, ensuring high-quality delivery under pressure in a fast-paced startup environment.

As an MLOps Team Leader, you will own our AI infrastructure and backend engineering efforts, leading a team at the intersection of systems design and machine learning to build the engineering systems that turn Deep Learning and Computer Vision research into reliable, scalable production features.

What Youll Be Doing:
Lead MLOps Engineering: Own the roadmap and execution for a team of backend and AI infrastructure/MLOps engineers, setting technical direction, removing blockers, and holding the bar on delivery quality.
Build Production AI Systems: Design and implement production-grade, end-to-end AI solutions, including agentic workflows, that integrate Deep Learning models and Computer Vision algorithms into real features. Own the offline experimentation and validation layer, including POC environments that let the Algo team move from research to production confidently.
Architect Data Platforms: Drive the design and implementation of scalable data platforms and pipelines that power our AI capabilities reliably and with an eye on cost and scale.
Cross-functional Collaboration: Work closely with the Algorithms and Data teams to tackle complex, real-world problems, translating research into shippable, maintainable systems.
Elevate Engineering Standards: Champion Software Engineering and System Design best practices across the group, introducing the right methodologies, tooling, and culture of craft.

we are looking for a Senior Full-Stack Engineer.
What You'll Do:
Own features end to end: product conversation → schema → API → UI → how it behaves in production.
Design the systems behind our AI agents - pipelines, orchestration, retries, guardrails, evaluation - so behavior is predictable and explainable.
Build backend services and data models that stay correct and stay fast as volume grows - across shared, single-tenant, and on-prem deployments.
Build the product surfaces our customers and our delivery teams use to configure, monitor, and audit agents.
Own your code in production: CI, deploys, observability, alerting, and the debugging that follows.
Work on how we ship, build and release pipelines, environments, and the infrastructure that lets one codebase deploy into very different customer environments.
Make the call on where complexity is worth it and where things should stay boring.
Raise the bar around you: code review, design review, mentoring, and the standards that keep a codebase alive two years from now.

We are hiring an Engineering Manager to own and grow the Data Science group - the R&D team behind our company's security and safety AI models and the data platform that powers them. You will lead a multidisciplinary team of ML engineers, data scientists, and software engineers who ship real-time inference at scale, automated red-teaming of GenAI systems, and the Databricks/Spark data platform underneath. This is a hands-on people-leadership role: you set technical direction, are accountable for delivery and quality, and you build and grow the team.
What your team owns
The group is responsible for a large Python + Rust + PySpark monorepo (dozens of production services and shared libraries) spanning three connected domains:
1. Content-moderation inference at scale
Real-time, multi-tenant detection across text, image, video, and audio - hate speech, CSAM, nudity, child grooming, extremism, PII, prompt injection, age estimation, and more - served through an in-house ActiveServe framework over NVIDIA Triton and a Rust detection monolith, on latency-sensitive, SLA-bound, customer-facing traffic with per-customer custom models.
2. GenAI safety & red-teaming
Automated red-teaming that attacks customers' LLM applications with a research-driven attack taxonomy and measures attack-success rate, alongside the defensive side - LLM-as-judge escalation to cut false positives and the tooling that authors and refines moderation policies - built on a multi-provider LLM foundation (Bedrock, Anthropic, OpenAI, Gemini, xAI Grok) and forming our company's leading edge into agentic-AI safety.
3. Data platform & MLOps
An end-to-end lakehouse and MLOps stack on Databricks - bronze/silver/gold ingestion, PySpark pipelines, and model training, versioning, and promotion through MLflow / Unity Catalog into Triton serving - with the performance-critical hot paths engineered in Rust (PyO3/maturin) for sub-millisecond, high-QPS matching and detection.
What you'll do
Lead and grow the team - mentor ML engineers, data scientists, and software engineers, and own hiring, onboarding, 1:1s, career development, and performance.
Set direction and deliver - set technical direction and standards, turn company and product goals into a prioritized roadmap, and own the quality, reliability, and delivery of the systems above across parallel workstreams.
Champion excellence and partnership - stay hands-on to review designs and unblock the team, drive engineering excellence (testing, observability, CI/CD, on-call, cost/latency), keep the team at the state of the art in ML, LLMs, and GenAI safety, and partner with Product, Platform, and GenAI-safety stakeholders.
Leadership competencies
People-first: builds trust, grows engineers, and creates a healthy, inclusive, high-ownership culture.
Outcome-oriented: drives clarity, sets priorities, and delivers under ambiguity without micromanaging.
Technical credibility: earns the team's respect through sound judgment on architecture and trade-offs.
Systems thinker: balances short-term delivery against long-term platform health, cost, and tech debt.

Alice is hiring a Senior Software Developer to lead the development of our internal malware research platform. This is a senior, hands-on role with end-to-end ownership of development and delivery. You'll be the technical authority — setting code-quality standards, making the architecture calls, and mentoring the developer team. What makes this role different is who you build for. Our users are our malware researchers, and they use the tool every day. Your job is to sit beside them, learn how they actually work, surface the heuristics and edge cases they carry in their heads, and build agentic tooling that compounds their productivity. The bar isn't "does it ship" — it's "do the researchers reach for it every day." Success is measured in researcher adoption and time saved, not features merged. Agentic workflows are core to how we build. You should be fluent using them and confident designing systems where agents run in production — with clear judgment about where an agent earns its keep versus where deterministic code or a human-in-the-loop is the right call. What You'll Do Lead development and delivery
* Own technical execution end-to-end: implementation, code review, and release.
* Translate research workflows and feature requests into well-scoped tasks with realistic, risk-aware estimates the team can plan against.
* Manage day-to-day execution: unblock people, sequence work, catch problems early.
* Set and defend the technical bar: review rigor, testing discipline, documentation, architectural consistency. Partner with the researchers — and amplify them
* Embed with malware researchers to understand their workflow and capture the tacit knowledge and edge cases no spec ever wrote down.
* Translate that knowledge into reliable agentic tooling — and know when an agent is confidently wrong before it ever reaches a researcher.
* Spend roughly 5–10% of your time doing actual malware research (with structured onboarding) to stay close to how the tool is used.
* Be willing to tell a researcher when a proposed workflow won't automate well — and explain why. Be the technical authority and mentor
* Make the hard architecture and design trade-off calls.
* Mentor through code review, pairing, and design discussions. Raise the level of everyone around you.
* Dive deep on the critical, difficult features and bug fixes yourself.
* Design agentic workflows into the architecture from the start, and build the evaluations and guardrails that keep them trustworthy.

About Alice:
Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact- whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection. Alice is widely considered a global leader in online safety and AI security. We have some of the most forward-thinking and passionate minds in the world working to safeguard over 3 billion users across the largest AI and tech platforms. If you're creative and driven to secure the future of AI, we want to hear from you!

We're looking for a smart and capable DevOps Engineer. You will be part of a small and agile team, working on well-built infrastructure, handling production workloads, building and maintaining infrastructure that supports continuous delivery. This job includes work across the devops stack, including agent development, CI/CD, networking, resource allocation, cloud architecture, security, developer experience, and more.
We work with enterprise customers, with the SLA and expectations that this dictates.
This position includes on-call shifts.
Our stack:
Kubernetes on AWS
Terraform and IAC
Github & Github Actions
Responsibilities
What will you DO:
Design and evolve our cloud multi-region architecture on AWS - networking, interconnects, IAM and high-availability topology - and express it entirely as code with Terraform, following GitOps as a first principle.
Build and own the CI/CD pipelines that plan, review, test and safely apply IaC changes - Policy-as-Code guardrails, drift detection and progressive rollout so infrastructure changes ship as confidently as application code.
Advance Platform-as-a-Product: build self-serve capabilities and paved paths so engineers can provision what they need
Strengthen our observability stack - metrics, logs, traces and alerting.
Troubleshoot and mitigate production incidents, participating in on-call rotations to ensure system health and stability.
Design and develop the next generation of AI agents to support higher pace and evolve the RnD productivity while maintaining security and engage the entire company to use AI for better solutions.

We're looking for an Automation Engineer to join the System Automation team, the central automation team within engineering department. SA owns the company-wide system automation platform: the system, correctness and performance tests, and the pipelines that run them, across products and a rich technology stack.
Our current focus is scale and performance. We test the limits of the system, benchmark it against realistic, large-scale workloads, and catch performance regressions before every release. We're also turning that expertise into a platform, with frameworks, tooling and know-how that let product teams write and run system tests themselves. You'll work closely with developers, architects, product managers, QA and DevOps engineers, and you'll help shape both the tests and the platform.
What you'll do:
Design, build and evolve the SA platform: the frameworks, tooling and CI pipelines that run system, correctness and performance tests across products.
Plan and run system-limitations testing to find where the product breaks under data volume, concurrency and load, and turn the findings into clear, actionable results.
Build and maintain performance benchmarks on realistic workloads, and run them on to catch regressions early.
Make it easy for product teams to own their system tests by creating reusable building blocks, documentation and examples, and by coaching engineers through knowledge-sharing sessions.
Analyze results with developers and architects to pinpoint bottlenecks and drive fixes
Use AI-assisted development tools and agentic workflows to speed up test creation and maintenance, and keep up with what's changing in automation and performance engineering.