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Location
Hybrid (Tel Aviv, Israel)
Employment
Full-Time

Confirmed on the employer's own hiring board on Nov 13, 2026. First seen by Alion on Nov 8, 2026.

Overview
Company
Impact
Profile match
AI21 Labs is an Israeli artificial intelligence company founded in Tel Aviv in 2017 by Amnon Shashua, Yoav Shoham and Ori Goshen to build language systems that reason rather than only predict text. It develops the Jamba family of models, which combine transformer layers with state space architecture to handle very long contexts far more efficiently than pure transformers, and releases several of them with open weights. The company sells through its developer platform and enterprise deployments, and has moved toward agentic orchestration with Maestro, a planning system that breaks complex business requests into verified steps.

Description

AI21 is an AI lab building frontier models and AI systems for the enterprise since 2017, and the only Israeli foundation lab that have trained LLMs from scratch.

Creator of the Jurassic and Jamba model families, AI21 today offers its lab-as-a-service for specialized intelligence: Forward-Deployed Researchers and a harness- and model-training platform that turn open foundations into models its customers own, delivered with the weights, recipes and evaluations that keep them theirs.

Role Overview

As an Applied AI Lead, you will be responsible for the successful design, execution and delivery of strategic, customer-facing AI projects from initial customer engagement and technical scoping through execution and production delivery. Operating at the intersection of technical customer leadership, AI expertise, and technical project management, you will guide an interdisciplinary team of engineers, algorithm developers, linguists, and infrastructure experts, driving alignment and execution. You are a technical leader and trusted customer partner, translating complex AI concepts, building deep client trust, and driving project scope, milestones, risks, and execution, while balancing resources, timelines, and quality.

Requirements

Key Requirements

  • Deep Technical AI Expertise:
  • Strong technical understanding of modern AI/LLM systems and model training processes, with the ability to engage credibly with engineers and algorithm developers.
  • Ability to navigate technical trade-offs and provide sound technical judgment across the AI lifecycle, including training, evaluation, data and inference.
  • Project Management:
  • Ability to define and drive scope, milestones, priorities, dependencies and success criteria, while proactively managing risks and blockers.
  • Strong execution and communication skills, with the ability to create clarity in complex and ambiguous environments.
  • Orchestrating multiple high-stakes, expensive projects and navigating internal and external stakeholders under tight schedules and technical uncertainty.
  • Technical Customer Relations: Driven by a genuine interest in customer success, you possess exceptional client-facing skills with a talent for managing high-profile relationships, establishing healthy communication routines, understanding nuanced customer needs, and building lasting trust.
  • Leadership & Cross-Functional Influence:
  • Proven ability to lead without authority across diverse, interdisciplinary teams while fostering strong internal routines and collaboration.
  • Working with internal and external senior management, including legal and finance, across different resolutions and depths.
  • Product Definition: Ability to define what a "good" model output looks like. Experience writing data specs, resolving behavior tradeoffs, establishing success metrics, and curating golden datasets.

About Us

AI21 is one of the leading, most experienced foundational AI labs in the world.

As companies start to deploy AI agents at scale, they face challenges rooted in missing expertise and ownership. We help companies scale their AI agents - sustainably. We automatically reduce token cost and waste without harming frontier quality, replacing weeks or months of manual tuning. This is achievable by applying advanced research and methodologies across the stack, from intelligent model routing and up to harness and model training.

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