AirOps is the first end-to-end content engineering platform built for the AI era. In a world where discovery is shifting from traditional search to AI-driven platforms, we help brands get found—and stay found. We are currently in a phase of hyper-growth, having 5x’d our revenue in the last year by helping marketing teams at Ramp, Chime, Carta, and Rippling turn content quality into a durable competitive advantage.
Our platform equips marketers to navigate the new discovery landscape, prioritize high-impact opportunities, and create accurate, on-brand content that earns citations from AI and trust from humans. Backed by Greylock, Unusual Ventures, Wing VC, and Founder Collective, we are building the intelligent systems that will empower the next generation of marketing leaders. AirOps is headquartered in San Francisco, New York and Montevideo.
About the RoleAs 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 ResponsibilitiesTechnical 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.
Qualifications5+ 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
Extreme Ownership
Quality
Curiosity and Play
Make Our Customers Heroes
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!
Skills Required
- 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
AirOps Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about AirOps and has not been reviewed or approved by AirOps.
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Healthcare Strength — Healthcare coverage is described as fully covered across medical, dental, and vision, minimizing out-of-pocket costs for employees. Feedback suggests this level of coverage is a standout element of the total rewards.
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Leave & Time Off Breadth — Time-off provisions include unlimited PTO alongside generous parental and family leave, supporting flexibility during both everyday and major life events. Feedback suggests these policies enhance work-life balance.
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Equity Value & Accessibility — Equity is consistently included across roles and positioned as a meaningful component of total compensation in a fast-growing startup context. Feedback suggests this upside strengthens overall pay perception.
AirOps Insights
What We Do
Build your AI growth engine. AirOps lets you easily build and scale AI workflows to crush your growth targets. Build with 40+ AI models, retrieval, and data sources or launch one of our proven playbooks.
