Applied AI Engineer, Data Workflows
Engineering · Full-time · San Francisco / Remote
Apply ↓Use modern AI systems to classify, enrich, QA, and package high-value datasets so they become more useful to model builders and AI product teams.
Sphere needs to turn messy real-world creator and enterprise data into structured, searchable, licensable supply. Applied AI is central to that workflow.
You will build practical AI-assisted systems for metadata generation, taxonomy mapping, data quality review, buyer matching, and workflow automation.
What you'll do
- Prototype and ship AI-assisted workflows for classification, summarization, tagging, deduplication, and quality checks.
- Evaluate model outputs, build review loops, and create tooling that makes human-in-the-loop operations efficient.
- Partner with data operations and engineering to automate repetitive curation and enrichment work.
- Translate buyer data requirements into productized workflows that can scale across categories and customers.
You may be a fit if
- You have experience building with LLMs, multimodal models, embeddings, evaluation workflows, or data pipelines.
- You can move from prototype to production and know when a simple deterministic system beats a model call.
- You are comfortable working with messy files, metadata, taxonomies, and ambiguous product requirements.
- You care about practical AI systems that create leverage for users and internal teams.