Lead Applied Scientist

AirOps helps brands get found and stay found in the AI era. As the first end-to-end content engineering platform, we give marketing teams the systems to win visibility across traditional and AI search with one durable advantage: quality.

Thousands of marketers use AirOps to see how their brand shows up across the new discovery landscape, prioritize the highest-impact opportunities, and create accurate, on-brand content that earns citations from AI platforms and trust from humans. We are building the platform and profession that will equip a million marketers to lead the next chapter of marketing, where creativity and intelligent systems work together and quality becomes the strategy that lasts.

AirOps is backed by Greylock, Unusual Ventures, Wing VC, Founder Collective, XFund, Village Global, Alt Capital, and more than a dozen top marketing leaders, with hubs in San Francisco, New York, and Montevideo.

As Lead Applied Scientist 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.

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.

• 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

1. Extreme Ownership

2. Quality

3. Curiosity and Play

4. Make Our Customers Heroes

5. Respectful Candor

• Equity in a fast-growing startup

• Competitive benefits package tailored to your location

• Flexible time off policy

• Parental Leave

• A fun-loving and (just a bit) nerdy team that loves to move fast!