Job Title: Applied AI Engineer, Stealth Co.

Company Name: 25madison

Job Details: RemoteFull,Time

Job Url: https://hiring.cafe/viewjob/w16k0mn3bq7brkmo

Job Description: Posted 9h agoApplied AI Engineer, Stealth Co.@ 25madisonView All JobsWebsiteUnited StatesRemoteFull TimeResponsibilities:build pipelines, integrate LLMs, improve modelsRequirements Summary:Strong Python, ML/NLP, LLM integration, and production pipelines; remote US work authorization.Technical Tools Mentioned:Python, Docker, Cloud Run, AWS, GCP, LLMs, NLP, OCR
Applied AI Engineer
Company: Stealth Co.
Role: Applied AI Engineer (Full-time)
Location: Remote (US, must have US Work Authorization)
Who We Are: We’re building the AI-native operating system for litigation. Today, legal teams face millions of siloed documents across PDFs, emails, and spreadsheets—making facts hard to connect and knowledge fragile. Our platform turns this chaos into knowledge graphs rooted in structured ontologies, compounding insights with every case. The result: faster strategy, stronger arguments, and a lasting edge in high-stakes litigation. 
About the Role: We’re hiring an AI Engineer to build and scale document processing pipelines, integrate LLMs, and ship AI-powered product features. You’ll work on real production systems where Legal Tech meets practical AI productization.
What you’ll do
Build, maintain, and scale document ingestion + processing pipelines (PDFs, structured/unstructured docs)Integrate and productionize LLM-powered workflows (extraction, classification, summarization, validation)Improve accuracy, reliability, and cost/performance of models and pipelinesCollaborate with product and engineering to turn customer needs into shipped featuresAdd evaluation, monitoring, and feedback loops to continuously improve outputs
Qualifications
Strong Python engineering skills (production-quality code, testing, debugging)Hands-on applied ML experience, especially NLP / document AIExperience with LLM integration (prompting, structured output, tool/function calling, retrieval)Comfortable owning features end-to-end: from prototype → production → iterationProduct-focused mindset (shipping, measuring impact, iterating fast)
Nice to haves:
Experience with OCR, layout parsing, entity extraction, or citation/provenance workflowsFamiliarity with cloud infrastructure and deployment (e.g., Docker, Cloud Run, AWS/GCP)Experience building evaluation harnesses for LLM quality (gold sets, metrics, regression testing)Prior work in legal tech, compliance, or other high-accuracy domains
To apply, submit your resume via breezy. Applications will be accepted on a rolling basis.