AI Research Engineer

Dropzone AI

$200K — $250K *
US-AnywhereRemote in United States
Enterprise Technology
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • 5+ years in software engineering, with at least 1+ year applying Generative AI in production
  • Proven experience building or researching agent frameworks or tool-using LLMs
  • Experience with memory and retrieval systems such as RAG and vector databases
  • Expert Python developer
  • Familiar with OpenCLAW and Claude Code harness architecture
  • Thrive in an early-stage startup environment with an ability to handle ambiguity

Responsibilities

  • Design and implement advanced multi-step reasoning agents, focusing on tool use and self-improvement
  • Develop frameworks for multi-agent coordination and task decomposition
  • Architect short-term and long-term memory subsystems for agents
  • Build mechanisms for context compression and retrieval
  • Define and implement evaluation frameworks for agent performance
  • Establish metrics and benchmarks for reliability in production
  • Translate the latest research into production-grade systems and contribute to technical direction

Benefits

  • 100% remote work with company-provided equipment
  • Semi-frequent travel for professional settings and events
  • Generous health insurance coverage
  • 401K Plan with employer match
  • Self-managed paid time off (PTO)
  • Parental leave
Full Job Description
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 12 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

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