Senior AI/ML Data Engineer

Job Description

Senior AI/ML Data Engineer 

The Senior AI/ML Data Engineer owns the production pipelines that turn unstructured text and documents into reliable, model-ready data for our team's Generative AI and Natural Language Processing (NLP) applications and services. Key focus areas will include engineering data products built from licensed scientific literature, patents, clinical trials, and more. You'll build and maintain ingestion and parsing pipelines for unstructured or semi-structured text data with data-quality checks built in, govern data lineage and usage compliance for AI applications, and configure the search indices built on this data. You'll also build shared pipeline components and deployment patterns used across the team. These data products support critical use cases spanning early-stage scientific research to post-market surveillance, including systematic literature reviews, competitive intelligence, target identification, and safety monitoring. 

You'll work directly alongside the data scientists and scientific subject matter experts who use your data. You will have the autonomy to propose better ways to build, see your work accelerate research, and ultimately help bring therapeutics to patients. As you grow, you'll have opportunities to expand into emerging areas, such as large language model (LLM)-powered document parsing and metadata enrichment. The position is embedded within a cross-disciplinary team of data scientists, bioinformaticians, and engineers dedicated to using cutting-edge AI/ML and data science techniques to drive drug discovery and development. 

What you will: 

• Pipeline delivery: Build and run production pipelines hands-on that turn unstructured text into reliable data products, and drive the technical execution of complex data initiatives 

• Requirements validation: Partner closely with stakeholders to clarify requirements and critically examine them for assumptions, feasibility, consistency, and completeness throughout the design lifecycle 

• Engineering standards: Shape modern data engineering practices across the team, including pipeline design patterns, data modeling conventions, schema governance, and resource optimization 

• Solution design: Write technical specifications that weigh architectural options, trade-offs, and risks, recommending the best-fit tools and approaches 

• Data quality and reliability: Embed automated validation checks, dataset versioning, and pipeline monitoring across data products, and establish robust data lineage to ensure traceability 

• Pipeline maintenance: Maintain and continuously improve production pipelines through dependency and platform upgrades, refactoring, and technical debt reduction 

• Data partnerships: Coordinate with upstream data providers and downstream data consumers to establish data contracts, dependencies, and validation datasets 

• ML productionization: Partner with the MLOps team to turn experimental ML pipelines into robust, production-grade workflows 

• AI-enabled development: Integrate coding agents into team workflows, such as automated pipeline-failure triage and fixes, with appropriate human oversight 

• Knowledge building: Mentor team members through design discussions and constructive code reviews, and translate technical knowledge into clear, comprehensive documentation and onboarding materials 

What you should have 

• 5+ years of professional experience in data engineering 

• 3+ years of experience building and operating production-scale data pipelines using Python, SQL, and Spark (or similar distributed frameworks) 

• Experience with workflow orchestration and CI/CD tools (e.g. Airflow, GitHub Actions) 

• Experience processing semi-structured or unstructured text data (e.g. XML, JSON, or scanned documents) in production environments 

Current Employees apply HERE

Current Contingent Workers apply HERE