Own end-to-end applied machine learning projects that automate accounting workflows. You will work across research-minded experimentation and practical engineering, building agent systems that can reason, plan, and improve reliably at scale.
Design and refine multi-agent architectures, including safety boundaries and fallback behaviors for accounting workflows.
Manage agent context, memory, and execution loops, and optimize model selection and routing against measurable goals.
Build scalable offline and online evaluation pipelines, define metrics and labeling strategies, and run structured experiments.
Instrument systems to detect regressions, analyze error patterns, and guide ongoing improvements.
Develop prompt stacks, retrieval pipelines, and document parsing systems that turn unstructured information into useful representations.
Build guardrails and validation layers, and test and communicate progress on end-to-end systems.
Four to twelve years of applied machine learning engineering experience, with deep expertise in Python and LLM or transformer-based systems.
Experience building end-to-end LLM agent applications, including benchmarking, model orchestration, and evaluation of agent behavior and reliability.
A track record designing multi-agent architectures and retrieval pipelines, and running structured ML experiments based on measurable results.
Industry experience building machine learning products in a fast-paced environment. Startup experience is essential; quantitative finance or leading technology company experience is also relevant.
Strong computer science fundamentals and a technical background in computer science, mathematics, physics, or a related field.
Clear communication skills and interest in AI's impact on accounting, finance, and the broader economy.
The salary range is $175,000 to $300,000 USD annually. Visa sponsorship is available.
On-site in New York, NY, five days per week. Candidates should be based in the United States or Canada, ideally on the East Coast.
Job details are sourced from the employer's original posting.
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