Senior Applied AI Engineer (NLP, Multi-Agent Systems)
Build production-grade AI systems, automate complex growth marketing workflows, and drive business outcomes with reliable NLP systems and multi-agent architectures.
Key Highlights
Technical Skills Required
Benefits & Perks
Job Description
Crossing Hurdles is a recruitment firm. We refer top candidates to our partners that collaborate with the world’s leading AI research labs to build and train cutting-edge AI models.
Role: Applied AI Engineer (NLP / Multi-Agent Systems)
Type: Full-time
Location: San Francisco, CA (On-site, 5 days/week; relocation supported)
Compensation: $180K – $220K base + equity
Visa Sponsorship: Available (visa & green card)
Role Overview
We’re hiring an Applied AI Engineer to build production-grade AI systems that automate complex growth marketing workflows end-to-end. This role focuses on reliable NLP systems, multi-agent architectures, and real customer impact, not demos or research-only prototypes.
You’ll own projects from 0 → 1, working closely with product, design, and engineering to ship AI features that directly drive business outcomes.
What You’ll Do
- Architect, evaluate, and optimize end-to-end NLP and LLM-powered systems
- Build and iterate on multi-agent architectures used in real production workflows
- Design systems that remain highly reliable despite LLM limitations
- Rapidly prototype, test, and productionize AI features
- Collaborate closely with product and design to ensure seamless user experiences
- Work across the stack as needed to take features to completion
- Establish evaluation and iteration loops to improve accuracy and consistency
Tech Stack
- Frontend: React, TypeScript, Next.js, Tailwind, Apollo GraphQL
- Backend: C# (.NET 9), EF Core
- AI / Orchestration: Python, LangChain, Semantic Kernel
- Data: Postgres (RDB, pub/sub, vector search)
- Infra: Google Cloud Platform
- Async / Workflows: Temporal
- Tools: GitHub, Linear, Slack, Notion
You’re a Strong Fit If You
- Have hands-on experience building production AI systems
- Understand LLM strengths, failure modes, and mitigation strategies
- Care deeply about customer impact, not just model quality
- Thrive in ambiguity and enjoy owning problems end-to-end
- Communicate clearly with engineers, designers, and product leaders
- Stay current with emerging AI tools, models, and architectures