Data Scientist / MLE

About the Role

As a Data Scientist / MLE at AirOps, you'll shape how brands win in AI-driven search environments through advanced machine learning and data science. This role combines technical depth with strategic thinking: you'll build production-grade ML systems that directly impact how companies create and optimize content for AI agents and improve their search visibility. You'll work at the intersection of NLP, search algorithms, and large language models to create solutions that help content teams drive measurable business results.

This is a hands-on leadership position where you'll both architect systems and write code. You'll partner with product, engineering, and customer success teams to identify opportunities where ML can transform our platform's capabilities. Your work will directly influence how thousands of brands adapt to the rapidly changing search landscape where AI shapes discovery and engagement.

Key Responsibilities

Technical Leadership: Design and deploy end-to-end machine learning systems including NLP models, search and recommendation algorithms, and LLM-based applications.

Search and Content Intelligence: Build ML systems that analyze AI search behavior, identify content opportunities, and predict performance across different AI-driven platforms. Create algorithms that help brands understand and optimize for how AI agents discover and rank content.

Cross-functional Partnership: Collaborate with product managers to translate business requirements into technical solutions.

Qualifications

• 5+ years building production machine learning systems with demonstrated business impact; strong background in NLP and search/recommendation systems required
• Deep expertise across ML approaches: classical models (XGBoost, random forests), modern deep learning architectures (transformers, graph neural networks), and reinforcement learning systems
• Proven ability to take models from research to production, including optimization for latency and cost at scale
• Experience with ML infrastructure and tooling: model serving frameworks, experiment tracking, feature stores, and monitoring systems
• Track record of technical leadership: influencing architecture decisions, improving team practices, and driving cross-functional projects without direct authority
• Excellent communication skills with ability to explain complex technical concepts to non-technical stakeholders and align ML initiatives with business outcomes

Our Guiding Principles

• Extreme Ownership
• Quality
• Curiosity and Play
• Make Our Customers Heroes
• Respectful Candor