Senior ML Engineer

Who you are

• Solid software engineering fundamentals and experience operating production-quality software
• Hands-on experience building ML- or LLM-powered applications—RAG, embeddings, agents, or probabilistic or ML-driven scoring
• Experience taking a prototype to a reliable, maintainable production solution
• Working knowledge of distributed systems, APIs, cloud-native development, and data or graph systems
• Strong Python, and/or Java, Go, or TypeScript
• Interest in security problems—vulnerability management, identity security, threat detection, or risk prioritization
• Experience with AI-assisted development tools and coding agents such as Claude Code, Codex, Cursor, or Windsurf is a plus
• Experience with AI evaluation, safety, or guardrails is a plus
• 3+ years of software engineering experience, or equivalent practical experience
• Experience designing and delivering production software systems
• Experience building or integrating AI/ML-powered applications in a production or near-production environment
• Modern AI experience: LLMs, RAG, embeddings, vector search, agentic workflows, model evaluation, or AI observability
• Strong programming experience in Python and/or Java, Go, or a similar language
• Cloud-native technologies, distributed systems, APIs, databases, and scalable architectures
• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical discipline, or equivalent practical experience
• Cybersecurity or security-product experience is a plus

What the job involves

• As a Senior ML Engineer, you build core components of a novel exploitability engine—shipping production ML that turns raw security signal into ranked, reachable attack paths. You take well-scoped problems from design to production and grow into deeper ownership as the system matures
• Well-scoped components of the engine—evidence ingestion and connectors, entity resolution, graph construction, or parts of the probability core—built to production quality
• The correctness and reliability of what you ship: tests, evaluation, observability, and the metrics that show your component works
• Turning ambiguous requirements into working code, with guidance on the calls that shape the wider system
• The data and model plumbing that keeps the graph accurate—entity-resolution quality, evidence provenance, and decay
• Design, build, test, and operate production ML components with strong engineering fundamentals
• Contribute to design and code reviews, and help raise the quality bar on the team
• Prototype quickly to evaluate new AI capabilities against real cybersecurity problems
• Partner with product, security R&D, and SecOps to understand the problem behind the ticket
• Apply AI safety, security, and guardrail practices to what you build

Benefits

• Generous family leave
• Matched donations
• Annual learning stipends
• Flexible PTO
• Competitive retirement plan
• Paid volunteer time