Founding ML / AI Engineer

Founding ML AI Engineer @ Lume Security

$120K–$230K · Founding Equity · Hybrid (ATL / NYC)

Early career & new grads welcome.

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Hey there. We're Cory and Adon, co-founders of Lume Security. Between us, we've spent 30+ years in security and AI at Google, Microsoft, IBM, and a handful of startups. We started Lume because we've lived the problem firsthand and believe security teams are too good to be this buried.

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About us

Security backlog is the attack surface. Those mundane tasks that keep getting deferred quietly compound — access reviews that slip, compliance evidence collection, updating procedure docs, onboarding cycles that drag, abandoned accounts that linger — until they show up at the center of an incident investigation.

AI hasn't fixed this. It made it worse. The copilot got faster. The task backlog got longer. The human is still the bottleneck.

Lume is different. We capture how your organization actually operates — its policies, its people, its institutional knowledge — and put it to work. AI agents embedded in the tools security teams already use automate entire categories of work. The agents get stuff done so the human can focus on what they’re great at - thinking creatively and exercising judgement.

We just came out of stealth. We’re building with customers. We raised our first round. Now we need you to keep up with demand.

What you'll work on

You'll build the intelligent systems that power Lume's platform — from model selection and fine-tuning to inference optimization and evaluation pipelines. This is applied AI engineering: your work ships to production and directly changes how security teams operate.

Design and build intelligent capabilities from the ground up. You'll own the full lifecycle — selecting the right models, building pipelines, and deploying systems taking action based on varied information sources.
Adapt and build evaluation and feedback systems. Our systems will continuously adapt and improve. You'll scope, adapt, and/or build the instrumentation, evaluation frameworks, and data pipelines that measure agent performance against real-world outcomes.
Fine-tune and adapt models for our domain. Techniques like fine-tuning, prompt and context engineering to make our models genuinely useful in production, not just impressive in demos.
Production integration. Reliability, latency, and scalability matter as much as model quality. You'll work across the stack to ensure our AI capabilities perform under real conditions.
Pattern Recognition. When patterns emerge you turn that signal into a better product and experience.
Establish engineering best practices. You're joining early. Every new engineer raises the bar and help us build a culture of technical excellence.

What you bring

Experience as a machine learning engineer or software engineer with a focus on machine learning.
Experience designing, building, and shipping production-grade AI systems.
Direct experience with fine-tuning, or evaluation techniques for ML models. You have data driven opinions about what works and what doesn’t.
Proficient in data pipelines and cloud infrastructure. You can build the plumbing, not just the model.