Applied AI Engineer, Autonomous Defense

What you'll do

• Develop reasoning systems to map attack paths and telemetry to control changes, assessing effectiveness and operational blast radius.
• Create training and evaluation data from pentest results and run counterfactual experiments to test remediation effectiveness and generalization.
• Design cross-vendor control-plane reasoning and safety systems for autonomous changes, including simulation, approval gates, staged rollout, rollback, and audit trails.

What they ask for

• Production AI/ML engineering experience, including deep learning, transformer models, and PyTorch; plus experience with LLM post-training or agentic systems with tool use, planning, and long-horizon execution.
• Track record building outcome-based evaluation systems for open-ended tasks and reasoning over structured, heterogeneous real-world data.
• Production-quality Python software engineering experience, plus experience with data pipelines, distributed systems, and cloud infrastructure.
• Master's in Computer Science, Machine Learning, or related field, or equivalent practical experience; 4+ years of professional engineering experience.

Nice to have

• Background in detection engineering, purple teaming, security engineering, offensive security, or incident response.
• Experience with causal or counterfactual inference, graph reasoning, planning, or production write-action automation with safety and change-management controls.

Pay

$313,000 - $369,000 annually (base salary); stock options (additional compensation; no amount stated)

Tools

PyTorch, Python, AWS, Neo4j, CrowdStrike, SentinelOne, Defender, Palo Alto

AI focus

Build and evaluate autonomous agents that reason over attack evidence, recommend and execute security-control changes, and verify fixes through real attack reruns; includes safety architecture, training data, and counterfactual evaluation.