Senior Member of Technical Staff, Applied ML
- Salary
- $175K–$300K
- Moves you to
- United States
- Support
- Visa sponsorship
- Posted
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About the Role
Own end-to-end applied machine learning projects for an AI-powered accounting platform. You will build agent systems that automate complex financial workflows, combining research-minded experimentation with practical engineering to deliver reliable systems at scale.
What You'll Do
Design and iterate on multi-agent architectures, including safety boundaries and fallback behaviors.
Manage agent context, memory, and loops, and optimize model routing against measurable success criteria.
Build scalable offline and online evaluation pipelines that automate experiments, metrics, and labeling strategies.
Instrument systems to identify regressions, track error patterns, and support continuous improvement.
Develop prompt and retrieval pipelines, and transform messy documents into structured representations for reasoning.
Build guardrails and validation layers, and test and communicate progress on end-to-end systems.
What We're Looking For
4 to 12 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 model orchestration, benchmarking, and evaluation of reliability and behavior.
A track record designing multi-agent systems and retrieval pipelines, and running structured experiments that use hypotheses and measurable results.
Experience at a fast-paced startup is essential; backgrounds in quant or hedge funds or leading technology companies are also relevant.
Strong computer science fundamentals and a technical background in computer science, math, physics, or a related field.
Clear communication skills and enthusiasm for applying AI to accounting, finance, and the broader economy.
Compensation & Benefits
Base salary: $175,000 to $300,000 USD annually. 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, ideally on the East Coast.