Remote opportunity at
VerticanSenior Applied AI Engineer, Document Intelligence
Vertican is seeking a Senior Applied AI Engineer, Document Intelligence to operate remotely within US time zones. For forty years, the company has developed software for…
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About This Role
Vertican is seeking a Senior Applied AI Engineer, Document Intelligence to operate remotely within US time zones. For forty years, the company has developed software for law firms, recently introducing an automated document platform that handles extraction, processing, and redaction. In this role, you will serve as the first dedicated machine learning hire on the product, working alongside the founding engineering team and taking over the current solution from the…
Job Description
Vertican is seeking a Senior Applied AI Engineer, Document Intelligence to operate remotely within US time zones. For forty years, the company has developed software for law firms, recently introducing an automated document platform that handles extraction, processing, and redaction. In this role, you will serve as the first dedicated machine learning hire on the product, working alongside the founding engineering team and taking over the current solution from the engineering manager.
The position is a senior, hands-on individual contributor role focused on owning end-to-end document processing pipelines from design and benchmarking to experimentation and deployment thresholds. You will establish evaluation processes, deploy components into cloud Python workers, manage costs and quotas, and collaborate with leadership on product accuracy and improvements. The ideal candidate builds custom tooling, thinks creatively about system architecture, and can determine the boundaries between traditional rules and machine learning models.
Responsibilities
- Design and own the architecture of extraction-to-posting pipelines including page classification, field extraction, candidate retrieval, claim matching, and confidence gating
- Evaluate new systems and components through structured trials such as probabilistic record linkage and Azure AI tools
- Establish and own the evaluation process for false positives, false negatives, and omission errors to determine pipeline upgrades
- Take components from trial to production in cloud-based Python workers, monitor operational metrics, and troubleshoot reviewer feedback
- Collaborate with leadership on planning for accuracy, cost optimization, and overall product enhancements
Requirements
- At least six years of experience building machine learning or data-intensive systems
- Minimum of three years owning a production pipeline whose outputs were actively utilized by people
- Solid understanding of backend engineering and cloud architecture principles
- Experience designing an evaluation process from scratch, including labeled ground-truth sets and metric selection
- Production experience utilizing LLM APIs for structured output, tool calling, and rate-limit management
- Strong proficiency in Python and SQL, with the ability to read Postgres query plans
Qualifications
- Hands-on experience with document understanding technologies such as OCR, layout models, or scanned PDF field extraction
- Experience with entity resolution or record linkage at scale against large databases
- Familiarity with Azure AI services, AWS equivalents, or GCP equivalents
- Background in financial document processing, accounts receivable automation, or payment matching
- Exposure to regulated workflows handling PII, legal operations, or collections
- Knowledge of probabilistic matching theory or active-learning labeling workflows
Core Skills
Benefits
- Competitive compensation matching a senior individual contributor position
- Monthly payments issued in USD
Frequently Asked Questions
Answers are based only on the employer’s listing; where it doesn’t say, neither do we.
Is this a remote position?
Yes, everyone on the team is remote in US time zones.
What is the salary for this role?
The source posting does not specify a salary, though it notes that compensation is competitive and commensurate with a senior individual-contributor role.
What level of experience is required?
Applicants must have at least six years of experience building machine learning or data-intensive systems, including at least three years owning a production pipeline.
Sample Interview Questions
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