דרושים » AI » head of ai

about the company:
the company is an ai-driven platform company focused on improving drug discovery and development by decoding the human immune system.
we combine large-scale single cell immune data, advanced Machine Learning, and strong engineering to help pharmaceutical and research partners make better, more informed decisions throughout the drug development process.
our long-term goal is to reduce drug development failure rates and help more effective medicines reach patients. were building this platform thoughtfully and collaboratively, bringing together expertise across biology, ai, engineering, and business.
the company is an equal opportunity employer. we celebrate diversity and are committed to creating an inclusive environment for all employees.
about the role:
we are looking for an ai infrastructure engineer to build the foundation that enables ai agents to reliably access and operate the company's internal systems, including code repositories, pipelines, datasets, and compute infrastructure.
you will develop scalable, secure, and standardized infrastructure for agentic workflows, working closely with computational biology and engineering teams to continuously improve how ai supports research and development.
this is a software infrastructure role, not a scientific modeling role, focused on building the systems that enable ai agents to operate effectively and safely.
location: ramat gan, israel (hybrid)
what will you do?
build agent infrastructure - design and maintain infrastructure enabling ai agents to interact with internal systems, data, and compute.
ensure quality & reliability - establish testing, ci/cd, and evaluation standards for agentic workflows.
implement safety controls - build permissions, approvals, and audit mechanisms for autonomous agents.
drive continuous improvement - monitor agent behavior and usage to improve reliability and performance.
scale agentic workflows - support reliable autonomous and multi-agent operations across teams and users.
optimize efficiency - manage performance, infrastructure costs, and compute utilization.
collaborate across teams - partner with research and engineering teams to enable ai-driven workflows.

about the company:
the company is an ai-driven platform company focused on improving drug discovery and development by decoding the human immune system.
we combine large-scale single cell immune data, advanced Machine Learning, and strong engineering to help pharmaceutical and research partners make better, more informed decisions throughout the drug development process.
our long-term goal is to reduce drug development failure rates and help more effective medicines reach patients. were building this platform thoughtfully and collaboratively, bringing together expertise across biology, ai, engineering, and business.
the company is an equal opportunity employer. we celebrate diversity and are committed to creating an inclusive environment for all employees.
about the role:
we are looking for an ml infra engineer to play a central role in evaluating, and deploying the company's ai models. you will own and evolve the company's 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.
this is not a core algorithmic research role - but rather, a role focused on building out the ml engineering infrastructure for the evaluation and deployment of models.
location: ramat gan, israel (hybrid model)
what will you do?
own & evolve benchmarking - design, build, and maintain the company's 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.
when you apply for a role at the company, we process your personal data (such as cv, contact details, and professional background) to assess your suitability for current and future roles. your information may be shared internally with relevant employees and stored or processed by trusted service providers, some of whom may be located outside your country of residence. you can read more about how we process data here: company.com/privacy-policy/.

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 a leading platform for Trust & Safety teams worldwide, leveraging cutting‑edge AI and world‑class expertise to protect users from the widest spectrum of online harms. By continuously collecting and analyzing data, our solutions ensure customers stay two steps ahead of bad actors in an ever‑changing threat landscape.
Were seeking an outstanding Backend Engineer with a Generative AI orientation to join our research team. We are building a new AI Platform from scratch to standardize AI development methods, ensure excellence and efficiency, and deliver robust, scalable, and compliant AI capabilities. This platform will handle GenAI models deployment, prompt management, data filtering, logging, and monitoring, centrally, so individuals can focus on their own business-specific logic.
Key Responsibilities
Build and maintain CI/CD pipelines that support fast, reliable integration and deployment of GenAI systems across complex environments, including orchestrating multiple models/agents and MCP clients/servers.
Develop, deploy and integrate AI solutions and agentic frameworks, creating both command‑line tools and interactive notebooks that support our teams research and workflow automation needs.
Build and manage data pipelines and databases (SQL) for large‑scale data handling, automating data collection and curation (e.g., adversarial prompts for model red-teaming) to support AI training and evaluation.
Integrate research into production by implementing the latest generative AI advancements (including white‑box LLM development), integrating different models and agents, and developing evaluation frameworks (e.g., agentic reasoning tests) as part of our automated workflows.
Integration & Orchestration: Integrate dockerized environments (e.g., Harbor format) into internal training frameworks, optimizing reset/statefulness semantics, concurrency, and throughput ceilings
Deploy GenAI models from repositories such as HuggingFace onto our AWS platform, connect them to data pipelines, and automate end‑to‑end processes for prompt generation, labeling, and response evaluation.

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.

We're looking for a Senior AI Engineer to design, build, and ship production-grade LLM agents that reason over workforce and skills data on top of Loomra's semantic layer. You won't just prototype - you'll own agent workflows end to end, from design through evaluation, deployment into the tools employees use daily (Teams, Slack, Copilot), and iteration in front of enterprise customers. You'll work alongside product, data, and platform engineers to turn powerful capabilities into reliable, safe, and fast product experiences.
If you've built agents that actually made it to production - and you care as much about evaluation, guardrails, and reliability as you do about capability - we want to talk to you.
Responsibilities
Design and build multi-agent systems and orchestration - intent routing, planning, tool use, and coordination across specialized agents.
Implement retrieval and RAG pipelines over structured and unstructured workforce data, grounded in our knowledge graph connecting people, jobs, and skills.
Integrate LLMs with tool/function calling and protocols such as MCP to give agents controlled access to HCM systems, business logic, and workflows.
Build evaluation harnesses, guardrails, and safety/bias checks, and work within the governance engine so agents behave reliably, respect customer policies, and produce a full audit trail.
Ship agents in a model-agnostic way across providers (Anthropic, Google, IBM watsonx) and deploy them into Teams, Slack, and Copilot.
Optimize agents for latency, cost, and reliability at enterprise scale.
Take agents from prototype to production - with monitoring, observability, and a fast iteration loop.
Partner closely with product, data, and platform teams to translate customer needs into agent capabilities.

Alice is a leading platform for Trust & Safety teams worldwide, combining cutting-edge AI with world-class expertise to protect users from the widest spectrum of online harms and keep customers two steps ahead of bad actors. We are looking for a Senior GenAI Data Scientist with strong hands-on experience in GenAI red teaming, adversarial testing, machine learning, and large-scale data analysis. You will develop the data science and ML capabilities behind an automated GenAI red-teaming platform. You will analyze and cluster adversarial prompts, identify emerging attack patterns, build models for automated prompt generation and mutation, and develop data-driven methods for prioritizing and evaluating security risks across different AI models. We are looking for a hands-on technologist with deep expertise in data science: clustering, scaling, machine learning, and statistical analysis. Key Responsibilities
* Build agents that act as data scientists at scale, reasoning, planning, and executing multi-step workflows to generate and analyze adversarial attacks.
* Manage and analyze prompt and response data from multiple sources (red-team campaigns, model evaluations, synthetic attacks); clean, curate, normalize, and label it for analysis.
* Analyze large volumes of structured and unstructured data to uncover trends, clusters, and anomalies, such as attack families, jailbreak techniques, and previously unseen patterns.
* Develop ML models and predictive algorithms to automate red-teaming.
* Build evaluation and monitoring pipelines to measure attack success and track model behavior across models and providers.
* Use statistical techniques and experiments to validate findings and ensure accuracy and reproducibility.
* Take research to production: build reliable, scalable systems optimized for cost and latency, with solid engineering practices (testing, CI/CD, telemetry).


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!