Member of Technical Staff, Applied ML

About the Role

Join an applied machine learning team building AI systems for accounting and financial workflows. You will own projects from initial design through production, helping agents reason, plan, and evaluate their performance safely and reliably.

What You'll Do

• Design and iterate on multi-agent architectures that automate complex workflows.

• Define agent autonomy boundaries, tool use, and fallback behaviors to improve safety and reliability.

• Build context and memory systems, and develop agent loops with measurable success criteria.

• Route, evaluate, and optimize models for latency, cost, and accuracy.

• Create automated offline and online evaluation pipelines, task sets, labeling strategies, and performance metrics.

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

• Develop prompt structures, retrieval and indexing pipelines, document parsing, and validation layers.

• Scope projects, write concise technical specifications, and build, test, and instrument systems end to end.

What We're Looking For

• Four to twelve years of machine learning engineering experience, including work in a fast-paced startup or similarly rigorous environment.

• Industry experience applying machine learning fundamentals to production products and models.

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

• Deep expertise in Python and LLM or transformer-based systems; experience with Postgres is relevant.

• Experience structuring ML experiments, defining hypotheses, building evaluation infrastructure, and iterating on measurable results.

• A technical degree and strong fundamentals, with clear communication skills and the ability to explain complex systems simply.

• Interest in applying AI to accounting, finance, and broader economic workflows.

Compensation & Benefits

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

Location

On-site in New York, NY, United States, five days per week.