# Data Scientist

**Company:** [AirOps](https://hotfix.jobs/companies/airops)
**Location:** San Francisco, CA
**Role:** Data Science
**Experience:** 5+ years
**Skills:** NLP, Xgboost, Transformers, Graph Neural Networks, Reinforcement Learning, ML Infrastructure, Model Serving, Experiment Tracking, Feature Stores, LLMs
**Posted:** 2026-05-12

> Builds production ML systems including NLP models, search/recommendation algorithms, and LLM applications to help brands optimize content for AI-driven search. Requires 5+ years experience with NLP/search systems and end-to-end ML deployment expertise.

## Job Description

## 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

## Benefits

- Equity in a fast-growing startup
- Competitive benefits package tailored to your location
- Flexible time off policy
- Parental Leave

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