Role OverviewWe are seeking a
Principal ML Architect to lead the design and development of
next-generation AI systems for cybersecurity, leveraging
state-of-the-art LLMs/SLMs and advanced machine learning techniques.
This role requires
deep expertise in model architecture, training, fine-tuning, and distillation, combined with a strong understanding of
security domains such as threat detection, anomaly detection, data protection, and AI safety.
You will drive the development of
intelligent, security-focused AI systems and agents capable of operating at scale across
high-volume, adversarial, and sensitive environments, while ensuring
robustness, explainability, and compliance.
Key ResponsibilitiesAI Architecture for Security Systems- Design and lead architecture for AI-driven security platforms, including:
- Threat detection and behavioral analytics
- Data loss prevention (DLP) and insider risk detection
- AI usage monitoring and policy enforcement (GenAI security)
- Build systems that process high-volume, high-velocity security telemetry in real time
Model Development & Innovation- Lead development of state-of-the-art SLMs/LLMs tailored for security use cases:
- Log analysis, alert triage, threat intelligence, policy reasoning
- Drive experimentation with modern architectures (Transformers, MoE, retrieval-augmented systems, hybrid models)
- Balance trade-offs between model accuracy, latency, interpretability, and cost
Training, Fine-Tuning & Distillation- Architect pipelines for:
- Domain adaptation and instruction tuning on security-specific datasets
- Model distillation and compression for efficient deployment in enterprise environments
- Design and execute experiments for:
- Alignment (RLHF/RLAIF) in security-sensitive contexts
- Red-teaming and adversarial robustness of models
AI Agents for Security Workflows- Design and oversee AI agents that:
- Automate security operations (SOC workflows, triage, investigation)
- Integrate with enterprise tools (SIEM, EDR, SaaS platforms)
- Define architectures for tool use, reasoning, memory, and policy-aware decision making
Experimentation & Evaluation- Establish rigorous evaluation frameworks for:
- Detection accuracy, false positives/negatives
- Model robustness under adversarial conditions
- Safety, hallucination, and misuse risks
- Lead deep experimentation cycles to continuously improve model performance and reliability
Productionization & Scale- Guide deployment of models into enterprise-scale, real-time environments
- Optimize inference systems for low latency, high throughput, and cost efficiency
- Collaborate with platform teams on ML infrastructure, data pipelines, and observability
Security, Governance & Responsible AI- Ensure models and systems meet enterprise security standards (SOC2, ISO, GDPR, etc.)
- Establish best practices for:
- Secure model development and deployment
- Data privacy and protection in training pipelines
- Responsible AI and model safety in adversarial environments
Required QualificationsExperience- 10+ years in ML/AI systems, with significant focus on deep learning and production ML
- Proven experience in:
- Building or scaling LLMs/SLMs or advanced ML systems
- Applying ML/AI in security, fraud, risk, or adversarial domains
- Track record of delivering production-grade AI systems at scale
Technical Expertise- Deep understanding of:
- Transformer architectures and modern LLM techniques
- Retrieval-augmented generation (RAG) and hybrid AI systems
- Model training dynamics, scaling laws, and optimization
- Hands-on experience with:
- Training, fine-tuning, and distilling models
- Efficient inference (quantization, pruning, batching)
- Distributed training frameworks (PyTorch, DeepSpeed, FSDP, etc.)
Security Domain Knowledge- Strong understanding of one or more:
- Security telemetry (logs, network traffic, endpoint data)
- Threat detection and anomaly detection systems
- Identity, access, and data protection systems
- Familiarity with security tooling ecosystems (SIEM, EDR, CASB, etc.)
Systems & Engineering- Experience designing high-throughput, low-latency ML systems
- Strong programming skills in Python, with production experience
- Understanding of data pipelines, feature engineering, and MLOps practices
Preferred Qualifications- Experience building AI systems for SaaS security or GenAI security platforms
- Familiarity with multi-agent systems for security automation
- Experience with synthetic data generation for security use cases
- Contributions to AI/ML research, open-source, or security tooling
- Background in AI safety, adversarial ML, or model interpretability
Success Metrics- Development of high-precision, low-noise security AI models
- Successful deployment of AI agents automating security workflows
- Measurable improvements in detection accuracy and operational efficiency
- Robustness of models against adversarial and real-world attack scenarios
- Strong adherence to enterprise security, privacy, and compliance standards
Why This Role MattersThis role is central to building
intelligent, AI-native security systems that can operate at
enterprise scale and under adversarial conditions. The Principal ML Architect will define the technical foundation for
secure, reliable, and high-performance AI systems, enabling the organization to lead in
next-generation cybersecurity powered by AI.
Base Pay Ranges:SF Bay Area, New York City Metro Area:
Base Pay Range: 254,000.00 - 349,250.00 USD
California (excludes SF Bay Area), Colorado, Connecticut, Illinois, Washington DC Metro, Maryland, Massachusetts, New Jersey, Texas, Washington, Virginia, and Alaska:
Base Pay Range: 208,800.00 - 287,100.00 USD
All other cities and states excluding those listed above:
Base Pay Range: 187,000.00 - 257,180.00 USD
