Location: NYC (Onsite in Flatiron)

Salary: Between 200-400k depending on levelling

About the Company

A rapidly growing AI startup is building a new-era Document Intelligence Platform. The core technology leverages cutting-edge Large Language Models (LLMs) to fundamentally change how major organizations interact with and extract value from large volumes of unstructured data. Its mission is to revolutionize the industry in intelligent document automation.

The Role: Machine Learning Engineer

The Machine Learning Engineer is responsible for engineering and deploying advanced AI systems and the underlying infrastructure necessary to process millions of client documents monthly. Their work will directly impact every customer, defining the future of intelligent document automation.

The company is looking for engineers who are current with the latest ML/AI research, think creatively about problems, and are motivated by building robust, impactful products rather than pure theoretical exploration.

Why Join

Accelerated Growth: The company has achieved multi-million dollar Annual Recurring Revenue (ARR) and saw its top-line revenue expand significantly over the last year, with rapid growth continuing to accelerate.
Mission-Critical Product: The platform is deeply integrated into essential business workflows for both enterprise and startup customers.
Market Opportunity: The demand for automated document processing has surged with the advent of LLMs, presenting a massive opportunity as many legacy solutions still lag behind.
High-Ownership Team: The team is a small, elite, founder-led group where every engineer enjoys significant autonomy and visibility.
Financial Stability & Upside: The business is well-capitalized with a successful institutional funding round that de-risks the business while maintaining substantial growth potential for early hires.
NYC In-Person Culture: The company operates primarily in-person from New York City with a talent-dense, collaborative culture.
Novel Technical Challenges: The company tackles unique, complex problems at the nexus of LLMs, data orchestration, and human-in-the-loop systems.

Example Problems the Engineer Will Work On

Developing self-monitoring agents to flag outputs with low model confidence.
Implementing closed-loop optimization systems to ensure continuous and automatic accuracy improvements in the background.
Designing multi-modal recognition systems for visual and structural document understanding.
Pioneering new data retrieval and indexing techniques for very long, complex documents.
Establishing reliable data integrity and model performance evaluation pipelines.
Building dynamic, adaptive systems that evolve based on live operational feedback.

Responsibilities

Research and design new LLM techniques to expand the complexity and scale of data the platform can handle.
Monitor production models, diagnose performance gaps, and run experiments to boost reliability and accuracy.
Own end-to-end projects—from initial concept validation through to production deployment and monitoring—with direct accountability for outcomes.
Collaborate closely with executive and founding leadership, contributing to overall product strategy and supporting technical hiring.

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