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Alice’s Innovation team builds adversarial RL environments that train the world’s 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. We’re looking for an AI 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. You’ll lead a small team (including a dedicated web environments engineer), operating with high autonomy, moving fast from concept to working prototype to production system. You’ll 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. You’ll be at the center of advancing AI safety, building systems that the world’s 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. There’s no playbook. You’ll write it. What you’ll 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 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.

Were looking for a Founding AI Engineer to build and define the research function at our company from zero. This is a rare opportunity to work on some of the hardest unsolved problems in applied AI and agents, with massive ownership, visibility, and room to grow into leadership quickly.
What Youll Do
Lead the AI domain at our company: Take ownership of our agent architecture and help define its next phases, standards, and direction.
Solve hard, unsolved problems: Work on challenges in AI agents, reasoning over complex systems, and autonomous decision-making that dont yet have clear playbooks.
Build production AI: Design, implement, evaluate, and deploy AI systems used by real customers.
Be outward-facing: Publish technical blogs, benchmarks, and papers; represent our company in the AI and data community.
Move fast with autonomy: Own problems end-to-end with minimal guidance, working directly with founders.
Deliver real customer value end-to-end: take features from idea to production, see them used by customers, and iterate based on real-world impact.

We are looking for a Staff Architect, Data & AI Infra to shape, build, and scale the infrastructure that powers data, AI, and research platforms. This is a senior player-coach role with broad architectural ownership across data infrastructure, ML infrastructure, developer experience, reproducibility, and production reliability. You will work across the wider engineering group as a hands-on technical architect, while also managing a small team of individual contributors focused on ML infrastructure.

This role is ideal for someone who can move between long-term platform architecture and practical execution: defining standards, building core systems, mentoring engineers, improving reliability, and partnering with Data Engineering, AI/Research, Product Engineering, Security, Bioinformatics, and Leadership to make data and AI platforms scalable, reproducible, secure, compliant, and easier to use.

Location: Ramat Gan, Israel (hybrid model)

What will you do?

Architectural Leadership: Own and evolve the technical roadmap for data and AI platforms, ensuring scalable and reliable architecture that supports current needs and prepares for a multi-cloud future.
MLOps & Platform Development: Design and build end-to-end MLOps systems-covering experimentation, training, reproducibility, and deployment-while managing specialized infrastructure like BigQuery, orchestration tools (Dagster/Airflow), and R/Python workloads.
Infrastructure Strategy: Define and lead strategy for GPU resources (scheduling, utilization, batch compute) and establish engineering best practices, data architecture standards, and platform guardrails.
Developer Experience: Enhance developer productivity by building self-service platforms, automation, internal tooling, and reusable templates that simplify workflows and reduce operational friction.
Team Leadership: Act as a player-coach to mentor engineers and manage a small team of ICs, fostering a culture of sound decision-making and technical excellence across the broader group.
Security & Reliability: Partner with Security to enforce compliance (SOC2, HIPAA, GDPR) and access controls, while mitigating operational risk through improved observability, incident readiness, and robust support processes.

Hands-on software engineering for our cutting-edge missile interceptor system. You'll develop and integrate components across the software stack, either on the Embedded / flight side (RTOS, low-latency sensor and comm code on the interceptor) or on the ground station / Command & Control side (distributed services for mission monitoring, threat evaluation, and visualization). You'll work under the Software Group Lead's architectural direction, with deep ownership of the modules you ship. We are looking for a "builder" mentality, someone who drives end-to-end delivery from concept to field deployment, and who treats AI coding tools as a force multiplier rather than a curiosity.
Why Join us?:
This is a unique opportunity to be at the heart of a defense-tech startup during its most exciting growth phase!
Responsibilities:
Build: Develop flight software, sensor fusion algorithms, and secure communication protocols between the interceptor and C2; or build distributed microservices for the ground station, depending on which area fits your background. Integrate: Work alongside Hardware Engineering (mechanical, electrical, GNC), Systems Engineering, and Program Management to integrate the software with the rest of the missile and ground station. TEST : Participate in HIL/SIL testing phases, support field trials, and write the automation that keeps these tests fast and reliable. AI-assisted delivery: Use modern AI coding tools (GitHub Copilot, Cursor, Claude Code, etc.) heavily across your daily work to ship faster without compromising safety-critical standards. Collaborate: Contribute to design reviews and architectural decisions; raise issues and propose alternatives rather than waiting for direction.

Hands-on software engineering for our cutting-edge missile interceptor system. You'll develop and integrate components across the software stack, either on the embedded / flight side (RTOS, low-latency sensor and comm code on the interceptor) or on the ground station / Command & Control side (distributed services for mission monitoring, threat evaluation, and visualization). You'll work under the Software Group Lead's architectural direction, with deep ownership of the modules you ship.
We are looking for a "builder" mentality, someone who drives end-to-end delivery from concept to field deployment, and who treats AI coding tools as a force multiplier rather than a curiosity.
Why Join us?
This is a unique opportunity to be at the heart of a defense-tech startup during its most exciting growth phase!
Responsibilities
Build: Develop flight software, sensor fusion algorithms, and secure communication protocols between the interceptor and C2; or build distributed microservices for the ground station, depending on which area fits your background.
Integrate: Work alongside Hardware Engineering (mechanical, electrical, GNC), Systems Engineering, and Program Management to integrate the software with the rest of the missile and ground station.
Test: Participate in HIL/SIL testing phases, support field trials, and write the automation that keeps these tests fast and reliable.
AI-assisted delivery: Use modern AI coding tools (GitHub Copilot, Cursor, Claude Code, etc.) heavily across your daily work to ship faster without compromising safety-critical standards.
Collaborate: Contribute to design reviews and architectural decisions; raise issues and propose alternatives rather than waiting for direction.

We are a fast-growing cybersecurity startup redefining how organizations protect themselves. Our Data Team is at the heart of that mission - powering everything from security insights to customer-facing intelligence. We are looking for a Software Engineer to help design and develop the core services and applications that drive our data capabilities. In this role, you will build high-quality, scalable software; contribute to the architecture of our next-generation data systems; and collaborate with research and product teams to turn cutting-edge ideas into production-ready features.
Responsibilities:
Design, develop, and maintain scalable backend services and components that power our data workflows, analytics, and product features.
Build high-quality internal tools and applications that improve engineering workflows and enable data-driven development across the organization.
Contribute to the architecture and evolution of our new data platform with a strong focus on clean design, testability, maintainability, and performance.
Collaborate closely with security researchers, analysts, and product managers to translate innovative cybersecurity concepts into reliable, production-ready software.
Apply engineering best practices across the stack - including code quality, testing, observability, versioning, and documentation - to ensure system robustness as we scale.

As a Senior DevOps Engineer on the Price-It Platform team, youll own core cloud infrastructure, CI/CD, and the Kubernetes platform that powers Earnixs cloud-based decisioning products for global insurers and banks. Youll take end-to-end technical ownership of high-impact platform initiatives - from design through production - working hands-on across AWS, Kubernetes, and automation, while shaping how the team builds and ships.
What You'll Do: Own and evolve the cloud platform: AWS infrastructure, EKS/Kubernetes, and Terraform-based Infrastructure-as-Code. Build and maintain CI/CD pipelines and GitOps workflows that keep delivery fast, safe, and repeatable. Design and operate secure networking and access (ALB, OIDC/OAuth2, JWT/M2M, Okta, firewalling). Drive reliability, scalability, and resilience - including disaster recovery and multi-region readiness. Partner closely with R&D, Product, QA, and Support to take initiatives from design to production. Improve Developer experience and velocity, including through AI-assisted and agentic engineering workflows. Mentor and raise the engineering bar through technical leadership, design reviews, and documentation.
Position Intro:
Earnix is the premier provider of mission-critical, cloud-based intelligent decisioning across pricing, rating, underwriting, and product personalization. These fully-integrated solutions provide ultra-fast ROI and are designed to transform how global insurers and banks are run by unlocking value across all facets of the business. Earnix has been innovating for insurers and banks since 2001 with customers in over 35 countries across six continents and offices in the Americas, Europe, Asia Pacific, and Israel.

We are seeking a highly motivated and experienced LLM/ML Agentic AI Researcher to lead the technical development of our agentic AI interpretation framework. This hands-on role involves designing, building, and evaluating AI agents that interpret complex biological data.

You will be at the forefront of developing a sophisticated scientific reasoning system that leverages Large Language Models (LLMs) to provide structured, biologically-grounded explanations. Collaborating closely with immunologists, machine learning researchers, and technical leadership, you'll shape how we derive insights at a systems level, pushing the boundaries of AI in biology.

Location: Ramat Gan, Israel (Hybrid role)

What will you do?

Design, prototype, and build LLM-based agentic systems that reason over biological data, scientific literature, model outputs, and internal tools.
Develop agents capable of structured reasoning, hypothesis generation, explanation, planning, tool use, and iterative scientific analysis.
Build robust evaluation frameworks for agentic systems, including automated and human-in-the-loop evaluation pipelines.
Define and implement benchmarks, metrics, and test suites for measuring agent performance, including reasoning quality, biological grounding, factuality, robustness, reproducibility, and usefulness.
Work closely with AI researchers, computational biologists, immunologists, and product teams to translate scientific needs into measurable AI capabilities.
Create evaluation datasets and benchmark tasks that reflect real-world biological and therapeutic reasoning problems.
Analyze agent behavior, failure modes, hallucinations, tool-use errors, reasoning gaps, and grounding issues.
Contribute to the architecture of production-grade AI systems, including agent orchestration, retrieval, tool calling, memory, planning, and monitoring.
Stay up to date with the latest developments in LLMs, agentic AI, evaluation methodologies, and scientific AI systems.
Help turn research prototypes into reliable products used by internal teams and external partners.

Were seeking a Head of AI to lead research and engineering for multimodal foundation models and reasoning‑centric systems-from prototyping to reliable, secure, production deployment. The Head of AI will manage deep learning researchers, computational biologists, immunologists and engineers and partner closely with product leaders and company leadership to convert cutting‑edge methods into high‑impact, mission‑critical capabilities.

Location: Ramat Gan, Israel (hybrid model)

What will you do?

Team Leadership & Growth

Lead, mentor, and develop deep learning researchers, computational biologists, and machine learning engineers; set a high bar for scientific rigor, code quality, and delivery.
Establish clear ownership, role definitions, and growth paths; nurture a collaborative, low‑ego culture.
Technical Strategy & Delivery

Own the AI roadmap and architecture; guide model design, evaluation, and productionization (training, serving, observability, safety).
Build scalable pipelines for transformers, multimodal fusion, and reasoning/agent frameworks; champion reproducibility, CI/CD for models, and cost‑efficient GPU/TPU utilization.
Research Integration & Innovation

Stay current with the literature; run journal clubs and technical deep dives.
Evaluate, pilot, and integrate state‑of‑the‑art methods (advanced transformers, retrieval and tool‑use agents, causal/biological reasoning) into robust systems.
Cross‑Functional Collaboration

Translate biological and clinical questions into tractable AI projects with measurable impact.
Communicate complex trade‑offs to peers and executives; align plans, risks, and milestones across functions.
Mission & Impact

Prioritize initiatives that advance patient outcomes and create cumulative platform value.
Balance speed with scientific integrity, security, and compliance.

We are looking for an AI Engineer to play a central role in building, evaluating, and advancing AI models. You will own and evolve 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, Israel (hybrid model)

What will you do?

Own & Evolve Benchmarking - Design, build, and maintain 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.