AI Research Engineer at Dropzone AI

Job description

About Dropzone AI Dropzone’s mission is to scale cybersecurity beyond human limits, and augment every single human security engineer/analyst with an army of AI security specialists. Humans alone cannot sufficiently protect our digital future, and AI augmentation is the only way for defenders to reclaim the high ground. We are an award winning company disrupting the $200B+ cybersecurity market. Powered by Gen AI advancements, our technology offloads repetitive day-to-day work and frees human analysts to focus on real threats and higher-value projects. We are venture-backed, and our team has a rare blend of deep experience across cybersecurity, AI/ML, and SaaS product development. Join us if you want to be on the ground floor of using Gen AI to transform cyber defense. Learn more at www.dropzone.ai . About the role We are seeking a Senior to Principal-level AI Research Engineer to lead the design and development of next-generation agentic AI systems. This role sits at the intersection of research and production, with a strong emphasis on: Agent architecture design Harness and memory engineering Robust evaluation and benchmarking of model and agent performance You will work closely with product and engineering teams to translate cutting-edge research into scalable, real-world systems. In this role, you will directly shape the core intelligence layer of Dropzone AI. Your work will define how our agents reason, remember, and improve over time, influencing both our product capabilities and the broader direction of applied AI systems. What we're looking for Someone who thinks in context/harness engineering, not just models A learner who can follow latest research and test them in real-world deployment Deep curiosity about how to convert non deterministic outputs from LLMs to consistent reliable outcomes and replicate expert human intuitions Strong ownership mindset and ability to drive ambiguous problems to clarity What you'll do Agentic Architecture Design and implement advanced multi-step reasoning agents (tool use, planning, reflection, self-improvement loops) Develop frameworks for multi-agent coordination and task decomposition Improve reliability, latency, and cost efficiency of agent execution Memory Systems Architect short-term and long-term memory subsystems (episodic, semantic, retrieval-based, hybrid) Build mechanisms for context compression, retrieval, and grounding Explore novel approaches to continual learning and state persistence Evaluation & Reliability Define and implement evaluation frameworks for agent performance (task success, reasoning quality, robustness) Build automated eval pipelines (synthetic data, adversarial testing, regression testing) Establish metrics and benchmarks for agent reliability in production Research → Production Translate latest community research ideas into production-grade systems Run experiments, analyze results, and iterate quickly Contribute to internal knowledge sharing and technical direction Requirements 5+ years in software engineering, with at least 1+ year applying GenAI in production Proven experience building or researching: Agent frameworks / tool-using LLMs Memory / retrieval systems (RAG, vector DBs, hybrid retrieval) Expert Python developer Familiar with openclaw and Claude Code harness architecture Early-stage startup mindset. You thrive on ambiguity and move with lightspeed execution Preferred Experience with agent orchestration frameworks (LangGraph, AutoGen, custom systems) Familiarity with AI safety guardrails, hallucination mitigation, and structured output enforcement Experience designing LLM evals (offline + online, human-in-the-loop, synthetic data) Publications or open-source contributions in relevant areas Experience applying latest context/harness engineering techniques to customer facing products Founder or early-stage (first 10 engineers) or experience in standing up a new technology bet within a more established company Work Environment/Travel We are a 100% remote company where you will work from your home with company-provided equipment to set you up for success. Semi-frequent travel to professional office settings and other events locally and nationally; some overnight travel expected. Compensation In the spirit of pay transparency, we are excited to share the base salary range below, exclusive of fringe benefits or potential bonuses. If you are hired at Dropzone your final base salary compensation will be determined based on factors such as geographic location, skills, education, and/or experience. In addition to those factors, we believe in the importance of pay equity and consider internal equity of our current team members as a part of any final offer. Please keep in mind that hiring at the maximum of the range would not be typical to allow for future and continued salary growth. We also offer a generous benefits package, including company paid health insurance, 401K Plan with employer match, Self-Managed PTO, parental leave, and more. The pay range for this role is: $200,000 — $250,000 USD

Requirements

Must-haves

Someone who thinks in context/harness engineering, not just models
A learner who can follow latest research and test them in real-world deployment
Deep curiosity about how to convert non deterministic outputs from LLMs to consistent reliable outcomes and replicate expert human intuitions
Strong ownership mindset and ability to drive ambiguous problems to clarity
Agentic Architecture
Design and implement advanced multi-step reasoning agents (tool use, planning, reflection, self-improvement loops)
Develop frameworks for multi-agent coordination and task decomposition
Improve reliability, latency, and cost efficiency of agent execution
Memory Systems
Architect short-term and long-term memory subsystems (episodic, semantic, retrieval-based, hybrid)
Build mechanisms for context compression, retrieval, and grounding
Explore novel approaches to continual learning and state persistence
Evaluation & Reliability
Define and implement evaluation frameworks for agent performance (task success, reasoning quality, robustness)
Build automated eval pipelines (synthetic data, adversarial testing, regression testing)
Establish metrics and benchmarks for agent reliability in production
Research → Production

Nice-to-haves

Experience with agent orchestration frameworks (LangGraph, AutoGen, custom systems)
Familiarity with AI safety guardrails, hallucination mitigation, and structured output enforcement
Experience designing LLM evals (offline + online, human-in-the-loop, synthetic data)
Publications or open-source contributions in relevant areas
Experience applying latest context/harness engineering techniques to customer facing products
Founder or early-stage (first 10 engineers) or experience in standing up a new technology bet within a more established company
Work Environment/Travel

Application prep

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Keywords to mirror

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Benefits

Benefits detected in the listing.

Health Insurance

401k

Pto

Parental Leave