דרושים » הנדסה » AI Engineering Tech Lead

We're looking for an experienced hands-on Software Engineering Team Lead to lead a highly skilled team of backend and frontend engineers through implementation, delivery, iteration, and build scalable systems that serve our customers.
Responsibilities:
Foster a positive, collaborative culture and grow the teams capabilities.
Lead and mentor engineers to ensure on-time delivery, quality development, productivity, and efficiency by conducting and lead design reviews & code reviews.
Work together with product managers to translate requirements into scalable technical solutions.
Balance refactoring and evolution with steady delivery of new features.
Drive a culture of technical excellence, ownership, and continuous improvement.
Oversee system reliability, monitoring, performance optimization and incident response.

Alice is building its internal AI infrastructure layer from the ground up. We have real agents running in production, a growing base of employees using AI in their daily work, and a clear architectural direction. What we don't have yet is a dedicated engineer to own it. You'll be the first. Your job is to close the gap between "working prototype" and "production platform" - owning the foundation that hosts our agents, the pipelines that ship them, and the reliability layer (observability, cost controls, audit trails, evals) that makes it safe to run AI at scale in a trust & safety company. This is an infrastructure-first role with deep AI fluency - not a prompt engineer, not a wrapper-framework operator, not a no-code builder. You should be equally comfortable writing a Terraform module, debugging a Kubernetes pod, and tracing an agent's tool-call chain. We don’t operate with a predefined backlog here; you will be responsible for identifying high-impact needs and bringing them to life. The perfect fit for this role has a track record of deploying agentic systems that have held up under real-world usage, balances a focus on infrastructure with a deep concern for user experience, and recognizes that the primary hurdle in AI integration is rarely the model itself. Responsibilities: Platform & Infrastructure
* Architect, build, and run the AWS/Kubernetes platform that hosts Alice's internal AI agents and tools; drive AWS Well-Architected pillars (operational excellence, security, reliability, performance, cost, sustainability).
* Own Infrastructure-as-Code: Terraform modules, standards, and reviews for Bedrock, agent runtimes, vector DBs, and supporting services. AI Systems
* Design and ship production-grade agents and multi-agent pipelines using the Anthropic Agent SDK, Claude Code, AWS Bedrock, and MCP — not wrapper frameworks.
* Own the full agent lifecycle: scoping ? prototyping ? eval ? deploy ? monitor ? iterate.
* Integrate agentic workflows into internal and product systems via APIs, databases, webhooks, Slack, and email. Reliability, Observability, Cost
* Build first-class observability across apps and infra: OpenTelemetry, Prometheus, plus LLM-specific tracing (Langfuse or equivalent), token/cost metrics, and eval pipelines.
* Define SLOs/SLIs and error budgets for AI services - latency, model fallback chains, eval regression gates, agent success rates. Lead incident readiness, response, and post-mortems.
* Drive FinOps: model routing by cost, cache hit rates, batch vs. realtime tradeoffs, budget alarms, per-team chargeback visibility.
* Implement guardrails: prompt-injection defenses, PII redaction, model allowlists, human-in-the-loop checkpoints, audit trails. Org Impact
* Identify high-leverage workflows across the organization and translate them into scalable agentic automations.
* Partner with R&D, Delivery, security, and external vendors to deliver platform capabilities.

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.

Hybrid:
Yes

As we scale our portfolio of live automations, were looking for an experienced AI Automation & Agent Engineer to take on a broad and high-impact role. Youll lead new automation projects from ideation to production, own the reliability of existing systems, and serve as the go-to expert helping our employees get the most out of AI tools - especially Claude Code. This is a hands-on, cross-functional position at the center of how we adopt and scale AI internally.
Responsibilities
Lead Automation Projects:
Partner with department leads across sales, support, finance, and HR to identify high-impact AI and automation opportunities.
Design and build LLM-based workflows, integrating APIs, MCP servers, and internal tools.
Develop agent-like automations and internal copilots that augment decision-making and execution.
Own the full lifecycle - from ideation and process design through development, testing, and production launch.
Present project plans, progress updates, and outcomes with measurable impact to stakeholders at all levels.
Build AI Systems & Integrations:
Build robust, maintainable workflows using N8N, Claude Code, and other orchestration tools.
Integrate across systems using REST APIs, webhooks, and external/internal tools.
Design reusable patterns for skills, agents, and workflows that can scale across teams.
Continuously evaluate and adopt new AI tooling, MCP capabilities, and agent frameworks.
Maintain & Improve Live Systems:
Monitor, triage, and resolve issues across all live AI automations, copilots, and agents.
Identify recurring failure patterns and implement systemic improvements to reliability, performance, and cost.
Ship incremental improvements and new capabilities quickly and safely.
Maintain clear documentation for workflows, agents, and system behavior.
Drive AI Adoption, Skills & Governance Across:
Act as the internal expert and first point of contact for employees using Claude Code and AI tools.
Help teams build and scale AI skills - from basic usage to advanced workflows and agent design.
Manage and optimize AI usage and performance across the organization (tokens, costs, reliability, adoption).
Build and evolve an internal AI control tower - providing visibility into usage, performance, governance, and impact.
Run onboarding sessions, workshops, and create practical guides that empower teams to work independently with AI.
Guide teams through MCP integrations, tool configurations, and best practices.
Stay current on Claude Code updates, new MCP capabilities, and emerging AI tooling - and proactively share relevant developments with the team.

We are looking for a strong Senior Software Developer to help build the algorithmic core of our platform. This role is ideal for a hands-on engineer with deep Python expertise, strong algorithmic thinking, and excitement about applying the latest AI, LLM, and agentic capabilities to real-world optimization problems.

You will work on systems that improve bidding, budget allocation, keyword strategy, audience optimization, and other core levers of marketplace and advertising performance.

What Youll Do

Design, build, and improve algorithms across bidding, budget allocation, keyword optimization, audience optimization, and related areas

Work closely with product, data, and engineering teams

Build systems that leverage the latest LLMs and agentic workflows as part of intelligent optimization and automation

Help shape the technical direction of the optimization engine and broader AI-driven platform

Contribute to architecture, code quality, testing, and deployment best practices for algorithmic Python systems

Required Data Engineering Manager
Description
We are one of the most popular and downloaded apps in the world. Working with us provides a unique opportunity to influence hundreds of millions of our users and to be part of the journey that makes us a super-app. Our mission is to make peoples lives easier by enabling meaningful connections, from precious moments with family and friends, through managing business relationships to pursuing their passions.
As an Engineering Manager in the data department, youll build and scale our data platform and data apps that powers our business insights. Youll design and implement robust pipelines to process billions of daily records, leveraging cutting-edge cloud technologies to transform data into actionable intelligence.
If youre passionate about data engineering and driving business growth through insights, wed love to hear from you!
Responsibilities
Lead and grow Data Engineering and Machine Learning teams in a high-scale environment (tens of billions of events per day).
Own the design and evolution of a self-service data platform enabling internal teams to easily build, ship, and consume data products.
Architect and scale batch and streaming pipelines powering core business and ML use cases.
Drive production ML systems end-to-end (recommendation, ranking, prediction) with direct business KPI impact.
Ensure reliability, scalability, and observability of large-scale data and ML systems in production.

We are looking for a highly skilled and motivated Software Team Lead to drive the technical direction of our development team while maintaining a substantial hands-on contribution. This role involves leading the team, hiring top talent, and ensuring the successful delivery of high-quality software solutions.
Responsibilities:
Technical Leadership: Guide the team in designing, developing, and maintaining robust cybersecurity solutions using Golang, C#, and Python.
Hands-On Development: Write high-quality, scalable, and secure code (50% of the time).
Team Management & Growth: Hire, mentor, and grow a team of talented engineers to build innovative cybersecurity products.
Code Reviews & Best Practices: Ensure high code quality through reviews, technical mentorship, and the enforcement of best practices.
Collaboration: Work closely with product managers, security researchers, and other stakeholders to define and deliver features.
Technical Roadmap: Contribute to architectural decisions and shape the future of the company's technology stack.

Our Innovation team builds adversarial RL environments that train the worlds most advanced AI models to be safer. Our customers are the leading frontier AI labs, who use these environments for post-training reinforcement learning and safety evaluation. This is the bleeding edge of AI safety technology: the environments you build will directly shape how next-generation models learn to resist adversarial attacks.
Were looking for a Principal software engineer to own the RL Gym platform end-to-end: from architecting multi-site web environments that simulate real-world attack surfaces, to optimizing our in-house orchestration harness (AgenticVerse) for high-performance delivery into customer training pipelines. This is a builder role. Youll lead a small team (including a dedicated web environments engineer), operating with high autonomy, moving fast from concept to working prototype to production system. Youll interact directly with customer engineering teams to understand their infrastructure constraints and deliver environments that meet their scale and reliability requirements. Why this role
This is one of the few roles in the industry where your code directly influences how the next generation of AI models are trained. Youll be at the center of advancing AI safety, building systems that the worlds top labs depend on to make their models more robust. The work is technically deep, the problem space is genuinely novel, and the field is moving faster than any team can keep up with alone. Theres no playbook. Youll write it.
What youll do:
Platform & performance
* Own and evolve AgenticVerse, our in-house orchestration harness that provisions and manages RL environments at scale. Focus on performance: low-latency provisioning, high concurrency, minimal overhead per environment instance
* Design and build isolated, reproducible web environments using Firecracker microVMs or Docker containers
* Architect multi-site scenarios (3-4 interconnected web applications per task) with rich interactions: drag-and-drop, file uploads, authentication flows, LLM-in-the-loop components
* Implement deterministic verifiers that evaluate agent behavior with zero ambiguity Customer delivery
* Work directly with engineering teams at leading AI labs to integrate RL Gym environments into their training and evaluation pipelines
* Translate customer specs into working environments, iterating rapidly on feedback
* Own the technical relationship: SLAs, API contracts, integration architecture
* Adapt environment delivery formats to cus tomer infrastructure ( Real-Time API calls vs. offline batch, managed vs. raw artifacts)
* Build customer-facing UIs when needed (dashboards, environment configuration portals, monitoring interfaces) Rapid prototyping
* Take ambiguous problem descriptions and produce working prototypes within days, not weeks
* Validate new environment types, interaction patterns, and verifier approaches quickly
* Build internal tooling that accelerates scenario authoring and testing
About us:
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, we provide 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.

As the Software Group Lead, you will be the primary architect and execution lead for a cutting-edge missile interceptor system. This is a high-stakes executive role requiring a rare blend of low-latency Embedded engineering and high-level Command & Control (C2) orchestration. You will be responsible for building a multi-disciplinary software team from the ground up, defining the software lifecycle, and ensuring the seamless integration of missile subassemblies with ground-based mission control. We are looking for a "builder" mentality, capable of switching between high-level architectural strategy and driving end-to-end delivery from concept to field deployment.
Why Join us?:
This is a unique opportunity to be at the heart of a defense-tech startup during its most exciting growth phase! The SW Group Lead will report directly to the CTO and act as a member of the company's leadership.
Responsibilities:
Team Leadership: Recruit, mentor, and manage a high-performance team of Embedded, Full-Stack, and GNC (Guidance, Navigation, and Control) engineers. system Architecture: Design the end-to-end software stack, from Real-Time operating systems (RTOS) on the interceptor to the distributed microservices of the ground station. Execution & Integration: Oversee the development of flight software, sensor fusion algorithms, and secure communication protocols between the interceptor and C2. AI-Driven SDLC: Implement and champion the use of modern AI coding tools (GitHub Copilot, Cursor, Claude Code, etc.) across the entire software lifecycle to accelerate development cycles without compromising safety-critical standards. Strategic Oversight: Align software milestones with hardware integration, testing phases (HIL/SIL), and field trials. Key Interfaces: Works closely with Hardware Engineering leads (mechanical, electrical, GNC), Program Management, and Systems Engineering teams internally; externally interfaces with defense customers, government program offices, and key technology vendors.

We are looking for a Senior Engineering Program Manager to join our global program management leadership team. In this role, you will lead the departments day‑to‑day operations, working closely with R&D leadership, Product Managers, and cross‑functional scrum teams. You will own complex programs and release lifecycles end‑to‑end, ensuring predictable delivery, transparency, and continuous improvement.

How Will You Make an Impact?
Lead complex, cross‑functional programs and release lifecycles from initiation through execution and successful delivery
Develop, manage, and maintain structured program and release plans
Monitor execution, proactively identify risks and dependencies, and drive mitigation plans
Provide clear, consistent status updates, dashboards, and reports to stakeholders
Ensure strong collaboration and effective communication across Product, R&D, and other cross‑functional teams
Enforce existing processes while identifying, shaping, and improving missing or ineffective processes
Drive data‑informed decision‑making by defining, implementing, monitoring, and optimizing KPIs
Act as an Agile champion and coach, fostering collaboration, accountability, and continuous improvement

We are looking for an AI Engineer to play a central role in building, evaluating, and advancing our AI models. You will own and evolve our benchmarking and evaluation capabilities for foundation models and multimodal systems, while also working closely with modeling teams to support model development, iteration, and validation. This role sits at the intersection of software engineering, model understanding, and applied AI, with broad influence on how models are built, compared, and improved across the organization.

Location: Ramat Gan (hybrid model).

What will you do?
Own & Evolve Benchmarking - Design, build, and maintain our benchmarking suite for foundation models and multimodal AI systems.
Define Core Abstractions - Create clean, extensible abstractions and APIs for datasets, tasks, models, metrics, and evaluation workflows.
Develop Metrics & Evaluations - Implement metrics that capture predictive performance, biological relevance, and multimodal alignment.
Support Model Development - Work closely with AI scientists and data scientists to integrate new models, tweak architectures, and enable rapid, fair iteration.
Bring in New Models & Baselines - Add external and internal models to benchmarks and ensure meaningful comparisons.
Explore Data When Needed - Dive into data and results to debug evaluations, understand model behavior, and unblock modeling work.
Enable Rigor & Reproducibility - Ensure evaluations are consistent, well-versioned, and trustworthy over time.