Senior Member of Technical Staff, Applied ML

About the Role

Join an applied machine learning team building agent systems that automate accounting workflows. You will combine research-minded experimentation with practical engineering to develop reliable AI systems for finance and accounting tasks.

What You'll Do

• Design and improve multi-agent architectures, including safety boundaries and fallback behaviors.

• Manage agent context, memory, model routing, and success criteria under real-world constraints.

• Build scalable evaluation pipelines for automated experiments, defining metrics and labeling strategies.

• Instrument systems to detect regressions, analyze errors, and support continuous improvement.

• Develop prompt and retrieval pipelines, including parsing unstructured documents into useful representations.

• Build guardrails and validation layers, and test and communicate the performance of end-to-end systems.

What We're Looking For

• Four to twelve years of applied machine learning engineering experience, with strong Python skills and experience with LLM and transformer-based systems.

• Experience building end-to-end LLM agent applications, including model orchestration, benchmarking, and evaluation of agent behavior and reliability.

• Experience designing multi-agent systems and retrieval pipelines, and running structured experiments with measurable outcomes.

• Strong computer science fundamentals and a degree in computer science, mathematics, physics, or a related technical field.

• Industry experience developing machine learning products in a fast-paced startup, quantitative finance, or similarly rigorous environment.

• Clear communication skills and interest in the impact of AI on accounting, finance, and the broader economy.

• Familiarity with Python and Postgres.

Compensation & Benefits

Annual salary range: $175,000 to $300,000 USD. Visa sponsorship is available.

Location

On-site, five days per week, in New York, NY. Candidates should be based in the United States or Canada, with preference for the East Coast.