AI/ML Engineer

Location: Planet Nine (IL)

Description

We are looking for a technical

AI/ML

to own the AI strategy and execution at Planet Nine. In this role, you will split your time between managing a small team of two AI researchers and remaining highly hands-on—actively architecting, training, and deploying models. 

You will serve as the primary AI authority within the company, responsible for inventing efficient solutions to novel problems. We need a pragmatic decision-maker who knows how to evaluate when to build models from scratch, when to fine-tune, and when to leverage existing technology to maximize impact. Since our projects focus on challenges without established blueprints, you will keep pace with recent AI developments and know how to integrate them effectively into our products.

Responsibilites

AI Ownership & Strategy:

Act as the primary owner for AI/ML initiatives across Planet Nine, aligning technical capabilities with our cybersecurity goals.

Hands-on Execution (60%+):

Take direct responsibility for the complete ML lifecycle—from data preparation and custom model training to evaluation, deployment, and production maintenance.

Problem Solving & Invention:

Invent and design machine learning solutions for complex, novel problems in threat intelligence and cybersecurity that lack established blueprints.

Pragmatic Architecture:

Evaluate and make calculated decisions on when to build proprietary models from scratch, fine-tune existing models, or leverage off-the-shelf APIs.

Team Management:

Manage, guide, and mentor a high-caliber team of two AI researchers, fostering a collaborative and efficient development culture.

Requirements

Experience:

4+ years of hands-on experience in Machine Learning (ML), Deep Learning (DL), and Large Language Models (LLMs).

Model Lifecycle Expertise:

Proven track record of both training custom models and effectively integrating or utilizing existing open-source and commercial models.

Modern AI Architectures:

Practical experience building and deploying applications using RAG (Retrieval-Augmented Generation) and MCP (Model Context Protocol).

Leadership:

Prior experience in a leadership or management role, with the ability to guide and mentor researchers.

Full-Stack ML/MLOps:

Solid understanding of the complete ML lifecycle, including MLOps, model monitoring, and production maintenance.

Advantages

Cybersecurity Background:

Experience in cybersecurity or security research is a huge advantage

Military Tech Units:

Alumni of elite technological military units (e.g., 8200, 81, or equivalent) is a big plus