AirOps is looking to solve the problem of helping brands win in AI-driven search environments by developing advanced machine learning and data science solutions. This involves building production-grade ML systems that impact how companies create and optimize content for AI agents and improve their search visibility, ultimately driving measurable business results.
Requirements
- 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
Responsibilities
- Design and deploy end-to-end machine learning systems including NLP models, search and recommendation algorithms, and LLM-based applications.
- 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.
- Architect systems and write code.
- Partner with product, engineering, and customer success teams to identify opportunities where ML can transform our platform's capabilities.
- Influence architecture decisions, improving team practices, and driving cross-functional projects without direct authority.
Other
- Technical Leadership
- Cross-functional Partnership
- Excellent communication skills with ability to explain complex technical concepts to non-technical stakeholders and align ML initiatives with business outcomes
- Extreme Ownership
- Quality
