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Remote mid-level role but highly specialized agent-research and systems requirements lower applicant density.
Requires specialized ML agent, research, and code-intel systems experience, limiting cross-industry transferability.
Multiple mandatory filters: 5+ years, 2+ agent-specific experience, publications/open-source, production system ownership.
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Research and develop autonomous agent architectures focusing on system-level performance improvements beyond the model itself.
Design and conduct controlled experiments on agent components like action space, memory schemas, evaluation methods to isolate failures and validate gains across different code repositories and model sizes.
Collaborate with alignment and interpretability researchers to analyze agent behavior and contribute outputs including research papers, benchmarks, tooling, and production-grade architectures.
At least 5 years in research engineering, systems, or ML infrastructure with minimum 2 years on agentic or LLM-based systems.
Proven ownership of complex systems from design to production behavior.
Strong experience with agent systems against real workloads, evaluation benchmark construction, code intelligence/program analysis, backend infrastructure (Python, containerization, distributed execution).
Public technical record such as first-author ML/systems publications, maintained open-source projects, benchmarks, or substantial write-ups.
Deep research judgment with ability to isolate system failures, design rigorous experiments, and validate results beyond artifacts.
Strategic expertise in agentic systems including current tools (ReAct, LangChain, etc), tool-use orchestration under partial failure, and distributed system observability.
Systems-level perspective balancing performance, reliability, cost, safety, and interpretability across loosely specified ambiguous problems with strong ownership mindset.