Two DotsSan Francisco, CAAs a Member of the Technical Staff, you will be a Software Engineer specializing in PDF processing and document understanding workflows. You will be responsible for building, scaling, and refining Python-based application code, ensuring fast and efficient PDF processing, and managing ML operations and quality.
We are looking for a Software Engineer with substantial prior experience working with PDFs and PDF-driven applications. PDFs are an odd legacy format: notoriously frustrating to work with, but critically important for understanding people’s finances. Many important businesses that used to run on paper documents now run on PDFs, including bank statements, paystubs, offer letters, and I-20 proof of F-1 visa documents.
This role is a strong fit for someone who has worked at companies that do OCR, and document understanding driven workflows.
You should be pragmatic. You should think less in terms of exploration alone and more in terms of: How will this perform? How will this scale? Is this simple? Is this reliable?
You should be an adept user of machine learning, with enough fluency to reason about model errors. You know what ROC, precision, and recall mean. You can reason through over-selection and under-selection, and compare false positives and false negatives against business needs.
The primary trait we are looking for is enough technical knowledge to execute without guidance when requirements are clear. You do not need to be a product engineer, but you should be able to prepare PDFs for machine learning steps and intelligently use those outputs to make full-stack updates to backend workflows that depend on them.
You should have a very strong command of Python, and a strong ability to measure service performance and accuracy with systematic metrics using SQL, such as BigQuery.
Machine learning and PDF processing often cross the infrastructure boundary in real-world applications. You should be comfortable debugging Kubernetes pods that are crash-looping or restarting, and understanding the impact of queueing, memory, disk usage, and CPU usage, without infrastructure being your sole focus.
As a Member of the Technical Staff, you will build consumer-facing chat agents that serve as the frontend to complex workflows. This role requires strong Python ability, user empathy, and a metrics-driven approach to understanding and improving chat agent quality.
Staff Data Scientist owns end-to-end development of ML and Generative AI solutions for the RiskOS fraud prevention platform, from data exploration and modeling to production deployment and monitoring. Requires 6+ years experience in data science with fraud/risk focus, Python/SQL proficiency, and GenAI expertise.
Staff-level AI engineer building and shipping LLM/agent-powered observability features that help users detect, triage, and resolve incidents. Requires strong production software engineering experience plus practical GenAI/LLM skills.
The Senior Member of Technical Staff, AI Quality will build and operate evaluation frameworks for production LLM systems, focusing on creating robust regression suites and monitoring tools to ensure the quality and reliability of AI agents.
Leads AI enablement across Checkr by gathering requirements, building custom LLM/RAG solutions, leading hackathons and training, and creating scalable workflows. Requires 5+ years engineering experience, 4+ years AI development, Python proficiency, and strong communication skills.