About Spikerz

Spikerz secures enterprises’ external digital assets, protecting business accounts, privileged access and brand presence across platforms beyond the corporate perimeter.

AI-powered attacks, compromised identities and ungoverned automation create new paths to account takeover and brand compromise. Spikerz combines enterprise-grade access governance with real-time threat detection and response to defend this expanding attack surface.

About the role

Build the intelligence that identifies emerging threats and powers security investigations. You’ll own applied AI from research and experimentation through production deployment, combining machine learning, contextual retrieval and AI agents to deliver measurable protection.

What you’ll do

• Develop detection models that recognize evolving attacks, combining content, account activity and identity context to identify phishing, impersonation and account compromise.
• Build AI agents for security investigations, enabling evidence collection, contextual analysis and response recommendations through tightly scoped tools and explicit authorization.
• Engineer contextual retrieval and security memory, using embeddings, vector search and reviewed findings to improve investigation accuracy while preserving tenant boundaries.
• Create rigorous evaluation pipelines covering multilingual threats, rare attacks, adversarial inputs and detection blind spots. Measure precision, recall and the operational impact of false positives.
• Own the production AI lifecycle—dataset quality, reproducible experiments, model and prompt versioning, controlled releases, drift monitoring and inference optimization.

What you bring

• 8+ years in applied AI/ML, including production systems whose performance you monitored and improved after deployment.
• Experience at a cybersecurity product company.
• Strong Python and software engineering skills, with practical experience building classifiers, inference services and data pipelines.
• Hands-on experience with LLMs, structured outputs, embeddings, RAG, vector databases and tool-calling agents.
• Strong evaluation fundamentals: representative datasets, leakage prevention, imbalanced classification, threshold calibration and systematic error analysis.
• Experience deploying and operating AI services, including observability, failure recovery, latency and cost optimization.

Modern AI engineering practices

• Build automated evaluations and adversarial regression tests into the release process.
• Protect against prompt injection, poisoned context and sensitive-data exposure; enforce tool permissions independently of model output.
• Use AI coding tools effectively while maintaining independent review, testing and technical ownership.

An advantage

Experience with multilingual NLP, phishing or fraud detection, Milvus, MLflow, Langfuse, Vertex AI, agent orchestration or MCP integrations.

Why Spikerz?

Turn advances in AI into defenses used by global brands, with direct ownership of detection quality and the next generation of security investigation capabilities.