Must be located in Raleigh, NC or willing to relocate to Raleigh, NC
About the Team:
LexisNexis Legal & Professional, which serves customers in more than 150 countries with 11,800 employees worldwide, is part of RELX (www.relx.com), a global provider of information-based analytics and decision tools for professional and business customers. Our company has been a long-time leader in deploying AI and advanced technologies to the legal market to improve productivity and transform the overall business and practice of law, deploying ethical and powerful generative AI solutions with a flexible, multi-model approach that prioritizes using the best model from today’s top model creators for each individual legal use case.
The company employs over 2,000 technologists, data scientists, and experts to develop, test, and validate solutions in line with RELX Responsible AI Principles (https://stories.relx.com/responsible-ai-principles/index.html).
Are you an experienced developer with a ˜can do™ attitude and enthusiasm that inspires others?
Do you enjoy being part of a team that works with a diverse range of technology?
This position performs complex research and data engineering assignments within an engineering functional area or product line, and provides direct input to project plans, schedules, and methodology in the development of cross-functional products. This position performs date engineering design - typically across multiple systems; mentors more junior members of the team; and talks to users/customers and translates their requests into solutions.
Responsibilities
Pipelines & preprocessing: scalable cleaning, OCR / layout normalization, early quality gating.
Labeling + active learning loop: strategic sampling, quality scoring, continuous feedback integration.
Training & inference engineering: sample automation, feature generation, resource orchestration, reliability & monitoring.
Serving & optimization: multi‑model routing, caching / indexing, elastic scaling, performance & cost efficiency.
Bachelor’s or above in Computer Science, Software Engineering, Information Systems, Data Engineering or related.
5+ years of experience in data / platform or backend engineering; practical ML or multimodal data project exposure.
Strong experience with data modeling, batch / streaming processing, distributed systems fundamentals.
Experienced with data cleaning & format transformation; multimodal sample construction & efficient storage.
Strong understanding multimodal training data patterns: balancing, segmentation, structural tagging, negative samples & quality metrics.
Experienced observability: integrated logs / metrics / tracing closed loop. SQL, data warehousing, object storage, columnar & vector index structures.
Demonstrates robust Python experience (data processing, concurrency / async, performance profiling, packaging & environment isolation). Linux CLI & bash scripting: files / permissions / processes, network & IO diagnostics, automation and troubleshooting.
Experience with cloud-based data platforms (e.g., AWS, GCP, Azure) for large-scale machine learning workflows.
Familiarity with MLOps tools and practices (e.g., MLflow, Kubeflow, Airflow) for
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