Thomson Reuters
Lead/Staff Software Engineer, AI
Are you ready to shape the future of AI‑driven tax automation? We’re looking for a Senior AI Engineer to build and scale intelligent workflows using modern LLM/ML tooling. Your work will directly impact how tax professionals automate complex, high‑stakes processes and access critical information.
As a Lead/Staff Software Engineer, you will own the design, implementation, and optimization of AI‑powered tax workflows—from data ingestion to production deployment—on our Tax and Accounting Professionals 1040 Scan product engineering team.
Responsibilities
Advance document intelligence: Build general‑izable models for tax documents using CV/NLP/LLMs and embeddings to move beyond fixed OCR templates with dynamic, context‑aware parsing.
Boost auto‑verification & quality: Improve native PDF/textlayer matching, anomaly detection, and prior‑year‑aware checks to catch issues before human review; design human‑in‑the‑loop flows that preserve practitioner control.
Scale the pipeline: Productionize low‑latency training/inference pipelines over millions of documents with robust observability, evaluation, and drift monitoring.
Integrate LLMs and traditional ML models into robust, auditable workflows that support tax determination, document processing, and rules‑based automation.
Build and maintain data pipelines, feature extraction, and preprocessing for tax‑relevant data (e.g., invoices, filings, transactional data).
Develop and integrate RESTful APIs / microservices to expose AI capabilities to internal and external systems.
Ensure solutions meet compliance, security, and auditability requirements typical of tax and regulated domains.
Collaborate across SurePrep engineering and other Thomson Reuters product & engineering teams leveraging TR’s AI platforms—including Materia and Additive—to deliver reliable, auditable AI in production.
About you
5+ years in applied ML/AI (or 5+ with an advanced degree) delivering production systems in document AI/OCR/NLP or information extraction at scale.
Strong Python and deep learning expertise (PyTorch/TensorFlow), with experience in LLMs, embeddings/vector search, prompt design/evaluation, and statistical methods for quality and uncertainty.
Cloud‑native ML ops experience: training/inference pipelines, feature/data stores, observability, cost/performance optimization.
Own end‑to‑end lifecycle for AI features: design, implementation, testing, deployment, monitoring, and iteration.
Optimize model/workflow inference for latency, throughput, and cost, including caching, batching, and model‑selection strategies.
Implement monitoring, logging, tracing, and alerting for workflows and models in production (data drift, model performance, error rates).
Debug and troubleshoot complex AI systems in production, including prompt failures, integration bugs, and data quality issues.
Partner with Product, Tax SMEs, and other engineering teams to translate business/tax requirements into AI workflows.
Collaborate with data scientists and ML engineers on model selection, evaluation, and experimentation within Reducto pipelines.
Lead design reviews, perform code reviews, and mentor mid‑level/junior engineers on AI best practices and production‑grade coding.
Contribute to internal standards, best practices, and templates for building AI workflows with Reducto.
Built end‑to‑end AI features running in production, not just prototypes.
Turn vague tax/business requirements into concrete, testable workflow designs.
Care about robustness and correctness as much as cleverness—especially in domains where errors have financial or compliance impact.
Comfortable reading tax/business rules and working with domain experts, even if not a tax specialist.
Preferred Qualifications
Document AI background: layout parsing, table extraction, doctype classification, native PDF matching, and verification automation.
Agentic/LLM orchestration experience with TR AI platforms such as Materia and Additive; retrieval‑augmented and tool‑use patterns; evaluation and guardrails.
Modern development practices: hands‑on AI coding assistants (Claude Code, Cline, GitHub Copilot) for rapid prototyping, code generation, and workflow automation.
Version control expertise. Experience with Git, testing frameworks (pytest or similar), and modern DevOps practices.
Programming experience in C#, Java, or any other language.
Exposure to tax/financial document workflows or other regulated content systems is a plus.
Salary and Benefits Base compensation range (US): $157,500 – $292,500. Additional compensation may include an annual bonus based on customer‑centered, enterprise, and individual performance. The package includes health, dental, vision, disability and life insurance, 401(k) with company match, paid holidays, paid leave, and various wellness and professional‑development benefits.
Thomson Reuters is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender identity, sexual orientation, national origin, or any protected characteristic. We provide reasonable accommodations for qualified individuals with disabilities and for sincerely held religious beliefs in accordance with applicable law.
About Us Thomson Reuters informs the way forward by bringing together trusted content and technology that professionals need to make the right decisions across legal, tax, accounting, compliance, and other sectors. With 26,000 employees worldwide, we empower innovation through flexible, inclusive, and supportive work environments.
Legal and EEO Statement Thomson Reuters complies with local laws that require upfront disclosure of the expected pay range for a position. The base compensation range varies across locations. If you have a valid reason for limitation or otherwise you can not provide facton specify a exception and you have an email Facial diagrams that step size or a meeting for Fire#0 and mighty legis the EEO and fairness. We also make reasonable accommodations for qualified individuals with disabilities and for sincerely held religious beliefs in accordance with applicable law. Learn more on how to protect yourself from fraudulent job postings here. More information about Thomson Reuters can be found on thomsonreuters.com.
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As a Lead/Staff Software Engineer, you will own the design, implementation, and optimization of AI‑powered tax workflows—from data ingestion to production deployment—on our Tax and Accounting Professionals 1040 Scan product engineering team.
Responsibilities
Advance document intelligence: Build general‑izable models for tax documents using CV/NLP/LLMs and embeddings to move beyond fixed OCR templates with dynamic, context‑aware parsing.
Boost auto‑verification & quality: Improve native PDF/textlayer matching, anomaly detection, and prior‑year‑aware checks to catch issues before human review; design human‑in‑the‑loop flows that preserve practitioner control.
Scale the pipeline: Productionize low‑latency training/inference pipelines over millions of documents with robust observability, evaluation, and drift monitoring.
Integrate LLMs and traditional ML models into robust, auditable workflows that support tax determination, document processing, and rules‑based automation.
Build and maintain data pipelines, feature extraction, and preprocessing for tax‑relevant data (e.g., invoices, filings, transactional data).
Develop and integrate RESTful APIs / microservices to expose AI capabilities to internal and external systems.
Ensure solutions meet compliance, security, and auditability requirements typical of tax and regulated domains.
Collaborate across SurePrep engineering and other Thomson Reuters product & engineering teams leveraging TR’s AI platforms—including Materia and Additive—to deliver reliable, auditable AI in production.
About you
5+ years in applied ML/AI (or 5+ with an advanced degree) delivering production systems in document AI/OCR/NLP or information extraction at scale.
Strong Python and deep learning expertise (PyTorch/TensorFlow), with experience in LLMs, embeddings/vector search, prompt design/evaluation, and statistical methods for quality and uncertainty.
Cloud‑native ML ops experience: training/inference pipelines, feature/data stores, observability, cost/performance optimization.
Own end‑to‑end lifecycle for AI features: design, implementation, testing, deployment, monitoring, and iteration.
Optimize model/workflow inference for latency, throughput, and cost, including caching, batching, and model‑selection strategies.
Implement monitoring, logging, tracing, and alerting for workflows and models in production (data drift, model performance, error rates).
Debug and troubleshoot complex AI systems in production, including prompt failures, integration bugs, and data quality issues.
Partner with Product, Tax SMEs, and other engineering teams to translate business/tax requirements into AI workflows.
Collaborate with data scientists and ML engineers on model selection, evaluation, and experimentation within Reducto pipelines.
Lead design reviews, perform code reviews, and mentor mid‑level/junior engineers on AI best practices and production‑grade coding.
Contribute to internal standards, best practices, and templates for building AI workflows with Reducto.
Built end‑to‑end AI features running in production, not just prototypes.
Turn vague tax/business requirements into concrete, testable workflow designs.
Care about robustness and correctness as much as cleverness—especially in domains where errors have financial or compliance impact.
Comfortable reading tax/business rules and working with domain experts, even if not a tax specialist.
Preferred Qualifications
Document AI background: layout parsing, table extraction, doctype classification, native PDF matching, and verification automation.
Agentic/LLM orchestration experience with TR AI platforms such as Materia and Additive; retrieval‑augmented and tool‑use patterns; evaluation and guardrails.
Modern development practices: hands‑on AI coding assistants (Claude Code, Cline, GitHub Copilot) for rapid prototyping, code generation, and workflow automation.
Version control expertise. Experience with Git, testing frameworks (pytest or similar), and modern DevOps practices.
Programming experience in C#, Java, or any other language.
Exposure to tax/financial document workflows or other regulated content systems is a plus.
Salary and Benefits Base compensation range (US): $157,500 – $292,500. Additional compensation may include an annual bonus based on customer‑centered, enterprise, and individual performance. The package includes health, dental, vision, disability and life insurance, 401(k) with company match, paid holidays, paid leave, and various wellness and professional‑development benefits.
Thomson Reuters is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender identity, sexual orientation, national origin, or any protected characteristic. We provide reasonable accommodations for qualified individuals with disabilities and for sincerely held religious beliefs in accordance with applicable law.
About Us Thomson Reuters informs the way forward by bringing together trusted content and technology that professionals need to make the right decisions across legal, tax, accounting, compliance, and other sectors. With 26,000 employees worldwide, we empower innovation through flexible, inclusive, and supportive work environments.
Legal and EEO Statement Thomson Reuters complies with local laws that require upfront disclosure of the expected pay range for a position. The base compensation range varies across locations. If you have a valid reason for limitation or otherwise you can not provide facton specify a exception and you have an email Facial diagrams that step size or a meeting for Fire#0 and mighty legis the EEO and fairness. We also make reasonable accommodations for qualified individuals with disabilities and for sincerely held religious beliefs in accordance with applicable law. Learn more on how to protect yourself from fraudulent job postings here. More information about Thomson Reuters can be found on thomsonreuters.com.
#J-18808-Ljbffr