AI Lead
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Before you apply
LocationSan Jose, United States
Work modehybrid
Typefull-time
SeniorityLead
Experience5+ yrs
DepartmentEngineering
EquityIncluded
Company size2+ people
PostedApr 27, 2026 · 6mo ago
Verified live20h ago
May no longer be actively hiring
Posted by the company on Apr 27, 2026
At a glanceSummarised by Seekless from the posting.
5+ years in machine learning / AI engineering (or equivalent depth)
Hands-on experience training or fine-tuning small or specialized language models
Strong understanding of Transformer architectures
Strong understanding of model optimization (quantization, pruning, distillation)
Strong understanding of evaluation and benchmarking
Experience in at least one area: network security / firewalls, endpoint or cloud security, application security or code analysis
Familiarity with threat detection systems
Familiarity with logs, telemetry, and security data pipelines
Familiarity with adversarial attack vectors
Strong programming skills (Python + ML frameworks like PyTorch/JAX)
Experience deploying models in production environments
Understanding of distributed systems and real-time inference constraints
Experience with edge AI or low-latency systems
Familiarity with agent frameworks / multi-agent systems
Contributions to open-source ML or security projects
Skills
PyTorch
JAX
Python
AI Lead (Small Language Models + Cybersecurity)
About the role
Location: San Francisco Bay Area (preferred) / Remote
Company: Protocol Nine
About the Role
We are looking for an AI Lead to build the core intelligence layer of an AI-native security platform. This role sits at the intersection of small language model (SLM) development, agentic systems, and cybersecurity, and will define how models reason about intent, risk, and behavior in real-world environments.
You will lead the design, training, and deployment of specialized models that operate in constrained, high-performance environments (e.g., edge, real-time inference), and apply them to security problems such as threat detection, policy enforcement, and autonomous decision-making.
What You’ll Do
Design & Train Small Language Models (SLMs)
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Build domain-specific models optimized for latency, cost, and controllability
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Fine-tune models on security datasets (logs, network traffic, code, policies)
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Develop techniques for distillation, quantization, and efficient inference
Build AI-Native Security Systems
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Architect models that reason about intent in security contexts
Develop detection systems for threats across:
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Network traffic
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Application behavior
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Integrate models into real-time decision pipelines (e.g., firewall, policy engine)
Agentic AI & Decision Systems
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Design multi-agent systems for continuous monitoring, analysis, and response
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Implement feedback loops between detection, reasoning, and enforcement layers
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Ensure reliability, explainability, and controllability of autonomous systems
Model Infrastructure & Deployment
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Optimize models for edge + distributed environments
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Build evaluation frameworks for adversarial robustness and false positives
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Work closely with engineering to productionize models (APIs, pipelines, scaling)
Security Research & Innovation
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Stay ahead of emerging threats (e.g., AI-generated attacks, supply chain risks)
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Experiment with novel approaches (e.g., semantic code analysis, intent verification)
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Contribute to technical strategy and product direction
What We’re Looking For
Core Requirements
5+ years in machine learning / AI engineering (or equivalent depth)
Hands-on experience training or fine-tuning small or specialized language models
Strong understanding of:
•
•
Transformer architectures
•
Model optimization (quantization, pruning, distillation)
•
Evaluation and benchmarking
Cybersecurity Experience
Experience in at least one area:
•
•
Network security / firewalls
•
Endpoint or cloud security
•
Application security or code analysis
Familiarity with:
•
•
Threat detection systems
•
Logs, telemetry, and security data pipelines
•
Adversarial attack vectors
Systems & Engineering
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Strong programming skills (Python + ML frameworks like PyTorch/JAX)
•
Experience deploying models in production environments
•
Understanding of distributed systems and real-time inference constraints
Nice to Have
Experience with edge AI or low-latency systems
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Familiarity with agent frameworks / multi-agent systems
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Contributions to open-source ML or security projects
Compensation
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Competitive salary + equity
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Early-stage ownership and high impact
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Opportunity to define a new category in security
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