AI/ML Data Engineer

Job Description

AI/ML Data Engineer 

The AI/ML Data Engineer builds and maintains 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 develop ingestion and parsing pipelines for unstructured or semi-structured text data, with data-quality checks built in. 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 be mentored by senior engineers and work directly alongside the data scientists and scientific subject matter experts who use your data. You'll gain hands-on exposure to how data feeds ML and large language model (LLM) systems, see your work accelerate research, and ultimately help bring therapeutics to patients. As you grow, you'll have opportunities to expand your skills in advanced pipeline performance tuning and lakehouse data modeling. 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 do:

• Pipeline delivery: Build and maintain production data pipelines, transformations, and automated workflows that turn scientific text into data products 

• Requirements validation: Clarify the requirements for your tasks, proactively identify gaps and assumptions, and raise questions before and during implementation 

• Engineering standards: Adopt and contribute to the team's data engineering practices, propose schemas that fit the team's data standards, and actively participate in constructive code reviews 

• Data quality and reliability: Build automated schema, completeness, and constraint checks into pipelines, and create test datasets for integration and regression testing 

• Pipeline maintenance: Monitor pipelines, troubleshoot operational failures, apply dependency and platform upgrades, and keep data products reliable and cost-efficient 

• ML productionization: Support the deployment of ML pipelines into production 

• AI-assisted engineering: Use coding agents in your daily development and contribute to the team's agent-assisted workflows, such as pipeline-failure triage 

What you should have 

• 3+ years of professional experience in data engineering or a closely related area 

• 2+ years of experience building data pipelines using Python and SQL 

• A Bachelor's degree in Computer Science, Data Engineering, or a related technical field, or equivalent practical experience 

Current Employees apply HERE

Current Contingent Workers apply HERE