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About the Role
Join an applied machine learning team building AI agents for accounting and finance workflows. You will own projects end to end, from defining success measures and running experiments to shipping reliable systems that help agents reason, plan, and evaluate their work.
Responsibilities
- Design and improve multi-agent architectures, including tool use, autonomy boundaries, and fallback behaviors.
- Manage context, memory, and agent execution loops with measurable success criteria.
- Route, evaluate, and optimize models for latency, cost, and accuracy.
- Build automated offline and online evaluation pipelines, define tasks and metrics, and track regressions.
- Develop prompt, retrieval, indexing, and document-parsing systems that provide agents with relevant context.
- Create guardrails and validation layers, then use experiments and production data to improve system reliability.
- Scope projects, write concise technical documents, build and instrument systems, and communicate progress with teammates.
Requirements
- Four to twelve years of machine learning engineering experience, including applied work on production products and models.
- Strong Python skills and deep familiarity with LLMs, transformers, and end-to-end agent applications.
- Experience with agent benchmarking, model orchestration, behavioral evaluation, and reliability.
- Experience framing ML hypotheses, building evaluation infrastructure, and iterating based on measurable results.
- Background at a fast-paced startup, quantitative finance organization, or other high-performing technology company.
- Strong technical fundamentals, clear communication, and the ability to explain complex concepts simply.
- Interest in applying AI to accounting, finance, and economic workflows.
Location
- New York, NY
Work Type
- On-site
Experience Level
- 4-12 years of machine learning engineering experience
Salary/Compensations
- $175,000 to $350,000 annually
Benefits
- Visa sponsorship is available.