This job has closed.

Thomson Reuters · 1 week ago

Lead/Staff Software Engineer, AI

Frisco, TX

Full-time

Hybrid

Senior Level, Lead/Staff

$158K/yr - $292K/yr

5+ years exp

Thomson Reuters is a leading provider of trusted content and technology for professionals across various sectors. They are seeking a Lead/Staff Software Engineer, AI to develop and scale intelligent workflows for AI-driven tax automation, focusing on enhancing document understanding and optimizing tax workflows.

AdviceAnalyticsFinancial ServicesManagement ConsultingProfessional ServicesRisk ManagementSoftware

H1B Sponsor Likely

Responsibilities

Advance document intelligence: Build generalizable models for tax documents (standard and non‑standard) using CV/NLP/LLMs and embeddings to move beyond fixed OCR templates with dynamic, context‑aware parsing

Boost auto‑verification & quality: Improve native‑PDF/text‑layer 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

You’ll 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

Own end‑to‑end lifecycle for AI features: design → implementation → testing → deployment → monitoring → 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

Qualification

Applied ML/AIPythonDeep LearningCloud-native ML opsDocument AIRESTful APIsData PipelinesVersion ControlSoftware EngineeringDomain FamiliaritySoft Skills

Required

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/perf optimization