Applied AI Engineering Lead
at Peregrinetechnologies
San Francisco, CA, senior, full time, USD 2,25,000–3,20,000 / year
posted 04 Feb 2026
- Training Pipelines
- Reinforcement Learning
- Model Serving
- Model Monitoring
- LLM
- Real-time Data Systems
- Failure Analysis
- Deep Learning
- Model Fine-tuning
- Drift Detection
- Model Rollback
- Batch Data Systems
- AI Infrastructure
- Inference Pipelines
- Model Versioning
About the role
Posted: 2026-02-04
Backed by leading Silicon Valley investors, Peregrine helps public safety organizations, state and local and governments, federal agencies, and private-sector institutions address society’s challenges with unprecedented speed and accuracy. Our AI-enabled platform turns siloed and disconnected data into operational intelligence — instantly surfacing mission-critical information to empower better, faster decisions that improve outcomes at every touchpoint. Today Peregrine supports hundreds of customers across 30+ states and two countries, serving more than 125 million people — and we’re amplifying our impact as we expand into the enterprise and internationally.
The Role
Peregrine builds an end-to-end intelligence platform. As the Applied AI Engineering Lead, you will own how artificial intelligence is applied, evaluated, and operationalized across our product, turning complex data into trusted, explainable, and high-impact capabilities used in the field.
This role is about production AI in high-consequence environments. You’ll lead to develop AI-powered features that help customers detect risk, surface insights, and make faster, better decisions while maintaining high reliability, transparency, and performance.
What you would do
Build AI-Powered Product Capabilities
- Lead the design and deployment of customer facing AI features across Peregrine’s platform
- Translate real operational problems into deployable AI-powered solutions
- Partner deeply with Product, Deployment Strategists, and other Engineering teams to integrate AI into core workflows
AI/ML Foundations
- Own and guide fine tuning strategies and reinforcement learning to improve decision quality
- Make principled tradeoffs between classical ML, deep learning, and LLM-based approaches based on accuracy, latency, and risk
AI Infrastructure
- Shape Peregrine’s AI infrastructure, including:
- Training and inference pipelines
- Model serving, versioning, and rollback
- Integration with real-time and batch data systems
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