# Applied AI Engineer

**Company:** [Zania](https://hotfix.jobs/companies/zania)
**Location:** San Francisco, CA
**Role:** ML Engineering
**Salary:** $180k – $250k/yr
**Experience:** 3+ years
**Skills:** LLMs, Fine-Tuning, RAG, Agentic Systems, Machine Learning, Ai Explainability, Ai Safety, Foundation Models, Prompt Engineering, Evaluation Metrics
**Posted:** 2025-07-20

> Builds production AI agents and workflows for governance, risk, and compliance, fine-tuning LLMs on proprietary data for high-accuracy tasks like regulatory analysis and risk assessment. Requires 3+ years applied AI experience with production ML systems emphasizing explainability and responsibility.

## Job Description

## What you'll do

- Build & fine-tune models. Fine-tune foundation models on proprietary data and implement novel techniques to achieve world-class accuracy on complex GRC tasks.
- Develop AI-native workflows. Build sophisticated, multi-step agentic workflows that automate complex GRC processes — from risk assessment to compliance monitoring to evidence collection.
- Champion responsible AI. Implement and pioneer methods for AI explainability and safety. Our systems must be transparent, auditable, and fair. This is non-negotiable in our domain.
- Drive innovation. Rapidly prototype, evaluate, and integrate state-of-the-art research (agents, RAG, new architectures) into reliable, production-grade features.

## Representative projects

- Build an AI agent that analyzes thousands of regulatory documents and internal controls, identifying compliance gaps with higher accuracy than a team of human experts.
- Develop an explainable AI system for risk assessment, allowing auditors and executives to understand and trust the AI's reasoning on high-stakes decisions.
- Build an advanced RAG pipeline over a massive corpus of unstructured company data to produce precise, verifiable assessments against complex compliance requirements.
- Partner with GRC subject matter experts to create ground-truth datasets for tasks like third-party risk evaluation, then fine-tune models that become the industry standard.

## What you have

- 3+ years in an applied AI or machine learning engineering role.
- Proven product sense. You've shipped reliable, production-scale ML products. You know how to use offline evaluation and online experimentation to achieve high-performance results.
- Hands-on applied AI expertise. Direct experience building with LLMs — fine-tuning, RAG, and agentic systems. Not theoretical. You've put these into production.
- High agency. You take full ownership of outcomes, move with a bias for action, and have a relentless drive for world-class accuracy. You see constraints as design problems, not blockers.
- Strong communication. You can work closely with security and GRC research counterparts and articulate technical tradeoffs clearly.

## Compensation & benefits

- Competitive salary + significant equity
- Flexible PTO
- Medical, dental, and vision insurance
- Meals and snacks in the office
- Relocation and immigration support

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