Senior Software Engineer

DLP's rule-based framework is broken. We asked machines to find problems with rigid rules, then expected humans to investigate the noise - an impossible task. Jazz is the new model. It starts with total coverage from a forensic endpoint agent and uses a powerful Agentic Investigator that learns your business. It delivers clear, pre-investigated answers instead of alerts, and is designed to be run by a single person. Finally, DLP that works.

Founded in 2024 and backed by leading cybersecurity VCs, we are already deployed with customers worldwide and growing quickly. We're a team obsessed with solving hard problems the right way, and we're looking for builders to join us.

We’re now looking for a world-class Senior Software Engineer to help architect and build the core systems behind our AI security engine.

As part of our AI Cybersecurity team, you’ll design and build the infrastructure and agent workflows that power Jazz’s security platform. This is a full-lifecycle engineering role, covering backend systems, AI agent development, and product integration, with a focus on building workflows that are reliable, scalable, and production-ready.

What You’ll Do:

• Architect and build backend infrastructure powering secure, scalable AI workflows.
• Develop core systems for multi-agent reasoning, retrieval pipelines, and tool execution.
• Productionize AI models and agent workflows with strong reliability and performance guarantees.
• Integrate AI services across the product stack, from backend APIs to user-facing workflows.
• Own systems end-to-end: design, implementation, deployment, monitoring, and iteration.
• Apply secure engineering principles across everything you build, from APIs and data flows to infrastructure and deployment.
  
  

• 6+ years of experience in backend engineering, with strong proficiency in Python and data-intensive distributed systems.
• Deep hands-on experience with relational and NoSQL databases (PostgreSQL, Redis, etc.), including schema design, query optimization, indexing strategies, and scaling for high-throughput workloads.
• Experience building and operating vector search and retrieval pipelines in production (pgvector, Pinecone, Weaviate, Qdrant, or similar), including embedding generation, indexing (HNSW/IVF), and relevance tuning.
• Experience building and operating scalable, reliable backend infrastructure (FastAPI, Kafka/Redis, Docker/Kubernetes, observability).
• Solid understanding of secure software design, including safe API development, data protection, and security-oriented engineering practices.
• Ability to own features end-to-end, from data modeling and architecture through implementation, deployment, monitoring, and ongoing improvements.
• Familiarity with LLM/AI concepts (agents, RAG, semantic search) and the ability to integrate these components with production data systems.
  
  

• Work on cutting-edge AI + cybersecurity challenges.
• Architect systems with massive technical depth and scale.
• High ownership and autonomy in an early-stage environment.
• Direct impact on product outcomes and engineering decisions.
• Collaborate with elite engineers and founders who have built large-scale systems before.