דרושים » תוכנה » AI Researcher - Agentic AI

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.

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 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.

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

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 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.

We are 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 deliveryף
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.

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.

The AI Engineering group builds modern infrastructure and solutions that improve how algorithms are developed at our company.
We are a small, independent team of experienced engineers with a mix of skills in algorithms, software, and infrastructure. We work in a DevOps style and build cross-team solutions that support research and development of advanced perception algorithms.
Our flagship project is a unified AV dataset used to train and evaluate next-generation models. We take large volumes of multi-camera video, object labels, HD maps, and sensor data from across the organization, and turn it into a curated, high-quality training set - at scale.
We are looking for someone who brings ML and computer-vision depth to the team - someone who can help shape the intelligence layer that decides what data is worth training on.
What will your job look like:
Work collaboratively with shared ownership. Your focus area will be the curation and ML side of our data pipeline, but you will contribute across the full stack alongside the rest of the team.
Build and improve the curation pipeline - from vision-model embeddings and scene detection, through VLM-based scene analysis, to scoring, deduplication, and sampling that produces a balanced and diverse dataset.
Run and optimize GPU inference at scale (embedding extraction, VLM inference) across thousands of driving sessions using workflow orchestration.
Develop scoring and sampling strategies that ensure rare but important scenarios (night driving, adverse weather, hazardous situations) are well-represented in the final dataset.
Work with algorithm teams to understand what data gaps hurt model performance and translate those into curation criteria.
Build validation and diagnostics that measure dataset quality - not just pipeline health, but whether the data is actually good for training.
Contribute to the core dataset SDK, converter, and 3D-geometry tooling (camera projection, calibration, coordinate transforms).