LexisNexis Legal & Professional is looking to develop state-of-the-art research tools to extract key information such as entities mentioned, sentiment analysis, data enrichments, predictive insights, and more to build best in class data and news streams relied on by their global customer base.
Requirements
- Solid foundation in machine learning / deep learning fundamentals, multimodal representations, and cross‑modal alignment concepts.
- Deep understanding of core principles and common algorithms for multimodal large models: cross‑modal attention & representation alignment, vision/text embedding fusion, hierarchical & layout structure modeling, instruction & contrastive paradigms, long‑context and retrieval‑augmented mechanisms, evaluation and failure mode dissection.
- Familiar with classic image and signal processing methods: edge & contour detection, filtering & denoising, morphological operations, segmentation & key point feature extraction, frequency / time‑frequency analysis, image enhancement & quality assessment;
- Knowledge of multi‑agent collaboration patterns: role assignment, task routing, feedback loops, redundancy & cross‑checks.
- Strong in statistical analysis & experimental design: hypothesis testing, factorial design, power analysis, A/B and multivariate evaluation.
- Able to decompose complex problems and build metric‑driven optimization paths.
- Rigorous in data quality & error analysis; rapid bottleneck identification.
Responsibilities
- Design and iterate the multimodal document parsing pipeline: layout / structural modeling, semantic extraction, cross‑modal alignment, structural reconstruction.
- Build and optimize a multi‑agent collaboration mechanism: task splitting, parallel / sequential scheduling, peer review, iterative quality improvement loops.
- Define model selection / composition / routing strategies (dynamic dispatch by document type, structural patterns, quality signals).
- Plan and execute model fine‑tuning, domain adaptation, continual learning, active learning, and data feedback loops.
- Establish end‑to‑end metrics: extraction accuracy, structural consistency, agent collaboration effectiveness, latency, stability, and cost.
- Build quality assurance and risk controls: drift & anomaly monitoring, confidence estimation, fallback strategies, alignment / compliance checks.
- Drive mapping and consistency between agent / model outputs and business knowledge field standards.
Other
- Education: Master’s degree or above in a quantitative or technical field (Statistics, Computer Science, Mathematics, Data Science, etc.).
- Experience: 5+ years of hands‑on machine learning / data science experience.
- U.S. National Base Pay Range: $102,800 - $171,300.
- Health Benefits: Comprehensive, multi-carrier program for medical, dental and vision benefits
- Retirement Benefits: 401(k) with match and an Employee Share Purchase Plan
