AI Systems Engineer

AI Systems Engineer

Location: Hybrid (Seattle Area Preferred)
Compensation: $150-300k, DOE + equity

About the Role

We're seeking an AI Systems Engineer to build the foundational systems that enable AI agents to continuously learn, improve, and automate complex enterprise workflows. This role focuses on creating the underlying intelligence layer that transforms human expertise into reusable knowledge, enabling AI systems to become more effective over time.

You will own the long-lived, stateful systems that power learning and decision-making, including knowledge representation, pattern discovery, workflow generation, and feedback-driven improvement mechanisms.

What You'll Own

• Design and maintain a knowledge graph that models complex relationships across enterprise environments in a queryable, AI-readable format
• Build and enhance pattern discovery systems that observe human actions, identify reusable workflows, and store learned knowledge with appropriate confidence scoring
• Develop workflow generation capabilities that convert observed resolution patterns into structured, executable runbooks for future automation
• Architect human review and active learning pipelines that determine when learned behaviors can safely transition from supervised to autonomous execution
• Design feedback collection and preference-learning systems that support future model improvement and domain-specific adaptation
• Partner closely with engineering teams to ensure knowledge systems integrate effectively with retrieval, memory, and reasoning layers
• Define evaluation frameworks and success metrics for learning systems, including accuracy, reliability, safety, and automation readiness

Required Qualifications

• 3+ years of experience building production AI, machine learning, or large-scale data systems
• Hands-on experience designing and implementing knowledge graphs, recommendation systems, learning pipelines, or similar intelligent systems
• Strong background in graph data modeling and schema design for complex real-world domains
• Experience building feedback-driven systems that improve through human input, preference learning, or active learning methodologies
• Familiarity with embeddings, semantic search, retrieval systems, and GraphRAG-style architectures
• Strong programming skills in Python and Java
• Experience building distributed or asynchronous systems with a focus on data quality and schema evolution
• Demonstrated ability to balance automation with safety, reliability, and human oversight

Preferred Qualifications

• Experience building backend platforms or full-stack applications
• Familiarity with ontology design, semantic data modeling, or technologies such as RDF, OWL, and SPARQL
• Background in enterprise IT, infrastructure, operations, service management, or related domains
• Experience with reinforcement learning from human feedback (RLHF), direct preference optimization (DPO), or model fine-tuning pipelines
• Knowledge of graph neural networks, graph-based retrieval systems, or structured reasoning architectures
• Experience developing evaluation frameworks for AI systems where correctness and generalization are difficult to measure

Why Join

This is an opportunity to work on foundational AI systems that enable intelligent automation at scale. You'll help shape the architecture, learning mechanisms, and evaluation strategies that determine how AI systems continuously improve and deliver value in production environments.