About the Role

Join an applied machine learning team building production AI agents for accounting and finance workflows. You will own projects end to end, from defining success and running experiments to shipping reliable systems that can reason, plan, and improve over time.

What You'll Do

Design and evolve multi-agent architectures that automate accounting workflows, with clear autonomy boundaries, tool use, and fallback behaviors.

Build context and memory systems, agent loops, and model orchestration that balance latency, cost, and accuracy.

Create scalable offline and online evaluation pipelines, define benchmarks and metrics, and use results to guide product and architecture decisions.

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

Develop prompt hierarchies, retrieval and indexing pipelines, and document parsing systems that provide agents with useful context.

Build guardrails and validation layers, and take projects from concise specifications through testing and delivery.

Communicate progress and findings clearly while collaborating with teammates and sharing technical knowledge.

What We're Looking For

Four to twelve years of industry experience as a machine learning engineer, including work at a fast-paced startup or comparable high-performing technical organization.

Strong Python skills and deep familiarity with LLMs, transformers, and production machine learning systems.

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

Experience framing structured ML experiments, building evaluation infrastructure, and iterating based on measurable outcomes.

Applied machine learning experience in industry, with a technical degree and strong fundamentals.

Clear communication skills and interest in applying AI to accounting, finance, and economic workflows.

Compensation & Benefits

Base salary: $175,000 to $350,000 annually. Visa sponsorship is available.

Location

On-site in New York, NY.