Staff Security Engineer, Detection & Response

About the Role

You'll own our Incident Detection and Response program, including threat modeling our systems and codebase and directing the investigation when something does happen. You'll join a team that includes an AppSec-focused engineer and an infrastructure-focused engineer, and you'll guide technical direction and judgment calls. You'll spend most of your time finding bugs in our code and gaps in our SDLC before they become incidents, then building and implementing or project managing the fixes yourself. AI is a growing part of that surface, and as our reliance on LLMs, AI-generated code, and agents expands, you'll be responsible for understanding and managing the risk that comes with it.

What You'll Own

Investigation & Technical Direction

• Lead investigations hands-on. When an incident or suspicious finding comes up, you pull the logs, build the narrative of what happened, and advise business and engineering leaders on next steps.
• Read production code when needed to understand what's actually happening.

Proactive Hunting & SDLC Hardening

• When you're not investigating, hunt for bugs in our codebase and weaknesses in our SDLC where problems can slip past existing controls.
• Propose and implement fixes yourself, or manage the resolution with the right teams, whether that's a specific code fix, a broader process or control change.
• Partner with our Product Security Engineer on golden paths when a weakness points to a systemic gap.

Detection Engineering

• Own and tune the detection logic running through 7AI, validating its investigations and escalations and setting the criteria for what triggers an alert.
• Close gaps in logging and visibility so the tooling has the right data to work with.

Managing AI Risk

• Help us understand and manage the security risks that come with our growing use of LLMs and AI tooling, both in what we build and what we adopt internally.

What We're Looking For

Required:

• 6+ years of security experience combining hands-on software or AppSec depth with detection and SIEM engineering ownership.
• Comfortable reading production code across multiple languages and stacks well enough to judge whether a finding is actually exploitable.
• Experience building threat models of codebases, reasoning about attack surface, trust boundaries, and data flow well enough to anticipate where problems will emerge.
• A track record of finding and fixing systemic weaknesses, whether they surfaced through an incident or through your own proactive review.
• Strong communication skills, with enough credibility to direct an investigation and influence peers informally.

Strongly preferred:

• Hands-on experience with AI-assisted security operations tooling, such as AI SOC platforms, LLM-based triage, or agentic escalation systems.
• Interest or experience in securing AI and LLM-powered systems, including risks like prompt injection, model misuse, or agentic tool abuse.
• Prior exposure to golden-path or secure-by-default infrastructure initiatives, even in a supporting role.
• Experience with container or image security and the patching lifecycle.
• A relevant certification such as GCIH, GCFA, GCDA, OSCP, or CISSP.

The base salary range for this role is $221,000 - $299,000 per year. You will also be entitled to receive equity and benefits. Individual pay decisions are based on a number of factors, including qualifications for the role, experience level, and skillset.

Maven embraces a flexible hybrid work model. Our teams primarily operate from the New York Metropolitan area, NY, and remotely via San Francisco/Bay Area, CA, Seattle, WA. For those in our New York City office, we encourage in-person collaboration by requiring team members to work onsite three days a week  (Tuesday, Wednesday, Thursday). For those based in Boston, DC, Chicago, Seattle, and San Francisco, we encourage in-person collaboration by requiring team members to attend monthly Work Together Days within these cities. This policy aims to balance remote work flexibility with the benefits of face-to-face interaction.