Senior Machine Learning Engineer (Multi-Agent Systems & AI Agent Orchestration)

Raydar • United State

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AI Summary

Lead end-to-end AI agent projects, designing autonomous systems for reasoning, planning, and self-evaluation. Build scalable evaluation pipelines, instrument error detection, and architect safe, reliable multi-agent architectures. Requires 4-12 years of LLM-based agent experience in fast-paced environments with strong Python and ML engineering skills.

Key Highlights

Own end-to-end AI agent projects with full autonomy in project planning, execution, and shipping

Design multi-agent architectures with safety, reliability, and tool-use boundaries

Build offline/online evaluation pipelines with metrics, labeling, and regression detection

Key Responsibilities

Design and iterate multi-agent architectures with autonomy boundaries, tool use, and fallback behavior for safety and reliability

Build scalable offline and online evaluation pipelines with golden tasks, labeling strategies, and metrics for hundreds of experiments

Instrument systems to detect regressions, track error taxonomies, and drive closed-loop improvements

Engineer context and retrieval systems, including prompt stacks, indexing pipelines, and structured document parsing

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Technical Skills Required

Python Machine Learning Large Language Models (LLMs)

Benefits & Perks

$175,000-$350,000 base salary

Highly competitive equity

Visa sponsorship available

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Nice to Have

Deep Python expertise

Interest in AI impact on professional services/economy

Job Description

About the Company

Our client is a Series B AI company, valued above $1B, whose agents do real work in a high-stakes, regulated professional domain. Founded in 2023, it has raised more than $100M, has grown from about 60 to roughly 150 employees this year and expects to keep growing quickly. The team is based in New York City.

About the Role

Own end-to-end projects that bring intelligence into production. You will be responsible for systems that help agents reason, plan and evaluate themselves, with full autonomy to plan projects, define success, run experiments and decide when a system is ready to ship. This role suits engineers who want to operate like researchers and builders at once.

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