Staff / Senior Staff Engineer, AI Products

We’re a team of ex-Google engineers who built some of the largest defensive platforms on the planet — Safe Browsing and reCAPTCHA. Now, we’re striking out on our own to tackle an even bigger challenge: stopping the new wave of adversarial AI attacks already hitting organizations today.

We're going after a $5B+ market, ripe for disruption. Traditional detection methods are too slow to keep up. Adversaries are using AI to craft customized, high-evasion attacks — and old-school rules-based systems don’t stand a chance.

We’re looking for a Staff or Senior Staff engineer to help build the next generation of security products at Aegis.

This is a highly technical role with a path to significant leadership. You won’t walk in and inherit an organization because of your title. You’ll start by owning hard problems, shipping, setting technical direction, and making the engineers around you better. As you prove that you can lead the product and the people building it, the scope can grow quickly.

If you’re a Staff or Senior Staff engineer who wants to build a team—or an engineering manager who wants to get closer to the technology before taking on much larger leadership scope—this role is designed for that trajectory.

Experience in one or more of:

• Security detection, fraud, abuse, or other adversarial systems

• Behavioral or anomaly detection

• Data-intensive distributed systems

• LLMs used in production decision-making

• Building evaluation systems and labeled datasets for ML or AI products

• Enterprise security products or integrations

• Taking an early product from a small number of users to broad production adoption

• Building or leading engineering teams in an early-stage company

Build security systems that can make good decisions from incomplete and noisy information.

Modern security products rarely get a perfect signal. They have to combine behavioral patterns, historical context, content, infrastructure signals, and other telemetry to determine whether something is actually risky—and decide what to do about it.

You’ll work on problems like:

• Turning large volumes of noisy security data into high-confidence detections.

• Influencing/Participating in model creation to build the best product

• Understanding what “normal” looks like when it is different for every user and organization.

• Moving new detection ideas from prototype to reliable production systems.

• Measuring whether the systems we build are actually correct.

The systems you build will operate in environments where false positives have consequences and latency, precision, reliability, and customer trust all matter.

You’ll work across detection systems, distributed infrastructure, product surfaces, and AI inference—and you’ll be expected to understand the full path from raw data to a customer-visible decision.

There is rarely a clean signal.
Real-world security problems are ambiguous. The same behavior can be harmless in one context and dangerous in another. The interesting engineering work is figuring out which signals matter and combining them into decisions you can defend.

“Normal” constantly changes.
Every organization behaves differently. Users change jobs, locations, devices, tools, and workflows. New customers arrive without much historical data. The systems need to learn without becoming brittle.

Precision is part of the product.
A security system that catches everything but constantly cries wolf isn’t useful. You’ll build evaluation systems, labeled datasets, replay infrastructure, and production feedback loops alongside the detection itself.

AI has to work in production.
It’s one thing to demonstrate an impressive model offline. It’s another to make AI part of a production system with real latency, reliability, privacy, and cost constraints.

Detection is only part of the problem.
The most useful security systems help customers act. Building automated or assisted responses that customers trust requires a much higher engineering bar than producing another finding in a dashboard.

Some of the product still needs to be invented.
You’ll have meaningful influence over what we build, which problems we prioritize, and how early technical ideas become products.

There is no giant organization between you and the problem. If you like ambiguous technical problems, fast iteration, and being responsible for whether the thing actually works in production, you’ll have a lot of fun here.

We want someone who is outgrowing their current scope.

Maybe you’re a Staff engineer and the path to meaningful leadership at your company is measured in years.

Maybe you’re a Senior Staff engineer who wants to do more than influence architecture from the side.

Maybe you recently became a manager and discovered you don’t want to choose between being deeply technical and building an organization.

Or maybe you’re already managing engineers, but your company isn’t moving fast enough and you want to get closer to the problems again.

At Aegis, the opportunity is to start as a senior technical owner and grow with the product.

At the beginning: you’ll be deeply hands-on—shipping code, understanding the systems, setting technical direction, and establishing credibility through execution.

As you prove the scope: you’ll increasingly lead projects and engineers, own a larger part of the technical roadmap, participate in hiring, and become responsible for how the team operates.

As the company grows: the role can expand into formal engineering leadership, with ownership of people, roadmap, hiring, and strategy.

The ceiling is intentionally high. We’re a Series A company building in a rapidly changing market. People who can repeatedly turn ambiguous problems into products—and make great engineers better around them—can take on dramatically more responsibility as the company grows.

Leadership here is earned through scope and results, not promised as part of the offer.

• You’ll work on problems at the intersection of AI and security. AI isn’t an add-on to the product; reasoning, learning, and adaptation are core to how we think about detection.

• You’ll build systems that make real decisions. This is production security engineering, where quality can be measured and the consequences matter.

• You’ll have unusual ownership. Small teams mean you can influence architecture, product direction, and customer experience instead of owning one narrow layer.

• You’ll work with people who have built defensive systems at massive scale. The team brings deep experience in security, distributed systems, and applied AI.

• You’ll move quickly. We value engineers who can go from an ambiguous problem to a working system without waiting for a large process to form around them.

We care less about whether your previous title perfectly matches this one and more about the scope you’ve already demonstrated.

You:

• Have operated at Staff-level technical scope, or have equivalent experience leading technically complex systems and teams.

• Have built production systems where false positives, correctness, reliability, or risk mattered.

• Can move between architecture and implementation without treating either as someone else’s job.

• Are comfortable making decisions with incomplete information and updating them quickly as you learn.

• Can prioritize across multiple important problems instead of treating every request as equally urgent.

• Communicate technical decisions clearly in writing.

• Make other engineers more effective, whether or not you have formally managed them.

• Want significantly more responsibility than your current environment can give you.

Most importantly, you are excited by the problems themselves. The leadership opportunity is real, but the path to it starts with being an exceptional engineer.

We’re small, technical, and ambitious.

You’ll have a lot of autonomy and a corresponding amount of responsibility. We care about measurable outcomes more than ceremony, clear thinking more than hierarchy, and people who can learn quickly more than people who already know every answer.

We expect senior engineers to write code, write down their decisions, talk to customers, challenge assumptions, and leave both the system and the team better than they found them.

If your current role feels too narrow, too slow, or too far removed from the problems that matter—and you want to see how much scope you can earn in a fast-growing AI-native security company—we should talk.