A Senior AI/ML Engineer role on our Data team, sitting at the intersection of AI engineering, data science, and data platform. You'll build agentic, LLM-based systems that turn vast amounts of unstructured information into high-quality structured signals - powering our products and surfacing legal risk - owning each solution across the full lifecycle, from exploration to production. You'll work closely with data engineers, data scientists, product managers, and legal experts to turn ambiguous problems into scalable AI capabilities. This is a hands-on role for someone with strong engineering judgment, deep data/ML expertise, and a focus on reliability beyond the prototype stage.
What You'll Do
Design, build, deploy, and operate production AI systems: agentic, LLM-based extraction workflows and batch/online inference pipelines and owning them from exploration through deployment, monitoring, and iteration.
Define quality metrics and build evaluation datasets, testing processes, and feedback loops for ML and LLM systems; monitor accuracy, latency, cost, and data quality, and investigate and fix failures.
Explore large, complex datasets to surface useful features and patterns, and translate findings into production-grade implementations.
Partner with data engineers on ingestion, transformation, orchestration, and storage to deliver dependable data products.
Use AI-assisted coding tools (e.g., Claude) effectively while maintaining strong technical judgment and ownership of your systems.
Help shape engineering standards for production AI: testing, observability, reproducibility, data lineage, versioning, and safe deployment.
Requirements: 6+ years in ML engineering, AI engineering, data science, or backend engineering for data-intensive systems, with hands-on experience taking systems to production (deployment, monitoring, evaluation, versioning, improvement).
Strong Python skills and data orientation - SQL, NoSQL, data pipelines, and both structured and unstructured data.
Solid data science foundation: feature engineering, statistical reasoning, experiment design, error analysis, and relevant metrics (precision, recall, accuracy, coverage).
Experience with LLM-based applications: structured extraction, tool calling, agentic workflows, prompt design, embeddings, retrieval, and model evaluation.
Understanding of production AI tradeoffs, quality, latency, scalability, reliability, cost and experience integrating AI into batch, orchestration, API, or event-driven workflows.
Ability to work independently in ambiguous environments, breaking problems down and driving them to production.
Strong communication skills across engineering, data science, product, and domain-expert teams.
Nice to Have
Document intelligence, information extraction, entity resolution, classification, ranking, or large-scale enrichment pipelines.
Experience building or operating agentic systems in production.
Natural Language Processing experience.
Familiarity with orchestration/ML tooling (Airflow, Dagster, Prefect, MLflow, or similar).
Experience with data warehouses, data lakes, vector databases, or modern data-processing frameworks.
Cloud-native experience: AWS, Docker, Kubernetes, IaC, CI/CD.
Experience with human-in-the-loop review or feedback-driven AI systems.
Familiarity with legal, financial, compliance, or other data-intensive domains where quality and explainability matter.
This position is open to all candidates.