AI Engineer

CipherData • Bellevue, WA

22d • Onsite

About The Position

You’ll join the team that owns AIDR’s core reasoning engine — the multi-agent layer that turns telemetry from 70+ security vendors into investigated, correlated conclusions an analyst can act on. You’ll design the agent architectures, run the experiments that decide which ones ship, and take them into production environments where SOC teams depend on the output. Success is measured in precision, recall, latency, and cost, not demo quality. That standard shapes how we work: Accuracy and honesty. We’re clear about what we’ve measured versus what’s still a hypothesis. We expect the same of your results — including when one doesn’t hold up. Research into production. We move new research into the product quickly, then validate it against operational standards rather than demo conditions. Measure, then decide. We experiment in small increments and make calls from metrics, not intuition. In practice, you’ll spend as much time building the evaluation harnesses that tell us whether an agent is good as you will building the agent itself. If you want to work on LLM systems where “is it good?” has a number attached, this is that role.

Requirements

• Hands-on experience designing and building LLM applications (agents, RAG, orchestration, etc.)
• Strong software engineering fundamentals in Python
• Experience building LLM workflows with various agent frameworks
• Experience evaluating and improving LLM output quality, both qualitatively and quantitatively — a real instinct for measurement, not just implementation
• Comfort defining ambiguous problems, experimenting quickly, and making decisions from data

Nice To Haves

• Security domain knowledge (SOC / SIEM / SOAR, threat intelligence, incident response, etc.)
• Experience designing evaluations/benchmarks, or an ML research background (publications, open source contributions, etc.)
• Experience operating production LLM systems (monitoring, cost optimization, latency management)
• Experience deploying production systems across cloud environments

Responsibilities

• Build our multi-agent LLM system: Design and improve the core system where multiple LLM agents collaborate to detect, investigate, and correlate threats.
• LLM orchestration: Build pipelines that coordinate multiple models against cost, latency, and accuracy trade-offs based on the task.
• Evaluation & continuous improvement: Create the evaluation systems that measure agent output quality, and design the loops that feed operational data back as a learning signal.
• Reliability: Experiment with and ship prompting, architecture, and verification strategies that reduce hallucination and improve stability and reproducibility.
• Build what’s next: Go beyond investigation into automated response, deeper threat correlation, and new product areas that push security operations further — built from the ground up, with you.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed