Senior Member of Technical Staff, Applied ML

About the Role

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.

What You'll Do

• 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.

What We're Looking For

• 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.

Compensation & Benefits

The salary range is $175,000 to $300,000 USD annually. Visa sponsorship is available.

Location

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.