What We Do

Cybercrime is growing, and more businesses are getting hit by threats that used to target only the biggest organizations. That pushes defenders like us to operate at the highest level, and it deepens our need for good people who want to make a meaningful impact.

Founded in 2015 by former NSA cyber operators, Huntress is a remote-first team working to make enterprise-grade cybersecurity accessible to businesses of all sizes. We work closely with security teams and service providers protecting complex environments, often without the time or headcount to handle it all. That’s why we build our technology in-house and back it with a 24/7 human-led Security Operations Center (SOC). As a result, our platform is never disconnected from the experts who manage it, ensuring our customers' protection.

Huntress now secures more than 6M endpoints and 17M identities worldwide. Those numbers keep growing because more businesses rely on us to help carry the load and operate with more confidence. Every day, you can see that commitment in how we stand with our customers and how we show up for each other.

Reports to: Director, Engineering

Location: Remote US, Only

Compensation: $165,000 to $190,000 base plus bonus and equity

What You’ll Do

Huntress is building an Agentic SOC. When our detection pipeline raises a signal, an LLM-based agent picks it up and investigates it end-to-end. It gathers telemetry across our products, reconstructs what happened, and makes a well-documented determination about whether the activity is malicious. Over time, it will take on more of the work itself, growing from resolving routine cases to acting fast when it finds an attack and handing analysts cases with the evidence already assembled.

This system is meant to augment our analysts, not replace them. It takes the high-volume, repetitive work off their plates so they can focus on complex cases and hands-on keyboard intrusions where human expertise matters most. It also reduces toil in the rest of their day by using LLMs to pre-process the output of the tools analysts use constantly, so they start from a clear picture instead of raw telemetry.

This isn’t a chatbot bolted onto a dashboard. The system investigates a large volume of signals a day across thousands of customer organizations. Many signals are benign, but some are live intrusions where minutes matter. The telemetry the system reads comes from environments where attackers are actively operating. Because the system makes real decisions, like closing an investigation or isolating a host, it has to be right, it has to show its work, and it has to know when to hand off to a person.

As a Senior Software Engineer on the Agentic SOC team, you’ll build and ship core parts of this system. You’ll work closely with the team’s technical leads, SOC analysts, product researchers, and product managers. You’ll own significant pieces end-to-end, from an ambiguous problem to a production feature you can show is working.

We make full use of AI coding tools to enable you to accelerate your development. We don’t cap token usage. We expect you to use AI and all tools responsibly.

Responsibilities

Build the investigation system. You design, build, and ship the components that make agentic investigation work: the tools agents use to query telemetry, the logic that drives an investigation, and the structured, evidence-backed output that analysts and customers see.
Turn analyst expertise into agent behavior. You sit with SOC analysts and product researchers to understand how they triage and investigate a given kind of Signal, then encode that into what the agent looks for, what it gathers, and how it explains its conclusions.
Take toil off analysts’ plates. You build LLM-powered features that pre-process the output of tools analysts use every day. They summarize, correlate, and highlight what matters, so analysts spend their time on judgment instead of assembly.
Measure what you ship. You build and extend the evaluations that tell us whether the system is getting better or worse, and you back your changes with evidence rather than a handful of good-looking examples.