Data Engineer (AI)

We’re hiring a Staff Data Engineer with a strong AI orientation to help build and scale the company’s data and AI platform.

This is a hands-on role with substantial ownership across data pipelines, production AI workflows, and system reliability. You will take initiatives from loosely defined problems through implementation, validation, and ongoing operation.

The right person combines strong data engineering fundamentals with practical AI experience, sound judgment, and a product mindset. You care about whether a system produces reliable, useful results-and take responsibility for making it work in production.

What You Will Own

Data Engineering and Reliability

Design, build, and maintain scalable batch and real-time data pipelines.

Improve data quality, consistency, and reliability across systems.

Resolve gaps between upstream data sources and downstream applications, with clear expectations for data behavior and ownership.

Diagnose pipeline failures, performance bottlenecks, and unexpected data changes.

Production AI Systems

Partner with Product and Engineering to deliver AI-enabled functionality.

Deploy, monitor, and troubleshoot LLM-based and machine learning workflows in production.

Turn models and experimental workflows into maintainable systems.

Evaluate outputs for reliability and usefulness, and address failure patterns before they affect downstream users.

Validation and Operational Excellence

Build validation frameworks, automated checks, and monitoring for data and AI output quality.

Apply statistical reasoning to identify anomalies, question assumptions, and investigate unexpected results.

Improve observability, performance, and operational reliability.

Establish practical safeguards for systems whose outputs can vary.

Technical Ownership and Execution

Translate ambiguous business and product needs into clear technical solutions.

Make pragmatic tradeoffs among speed, complexity, cost, and quality.

Use existing platforms and AI development tools where they improve execution.

Communicate technical decisions clearly and follow through from implementation to production support.

Data Engineering Experience

Strong experience building and maintaining production data pipelines.

Proficiency in Python, SQL, and modern data platforms.

Experience with Spark or comparable distributed processing systems.

Demonstrated ability to operationalize and support data systems or models in production.

Strong troubleshooting skills across data flows and system boundaries.

AI and Analytical Judgment

Practical experience working with LLMs, integrating AI tools, or supporting AI/ML workflows.

Understanding of LLM fundamentals and the implications of probabilistic outputs.

Experience using AI tools to accelerate development and problem-solving while independently validating results.

Sound statistical intuition and the ability to reason about data quality, anomalies, and output reliability.

Ownership and Collaboration

A product mindset: you connect technical work to user needs and business outcomes.

A habit of testing assumptions and validating outputs.

Pragmatic judgment about when to build, reuse, or simplify.

Comfort working through ambiguity in a rapidly evolving technical environment.

Clear, proactive communication and strong follow-through.

Ability to explain your approach and work effectively across Product, Engineering, and Data teams.

Help shape platform architecture and engineering practices as the company scales.

Work closely with technical and business leaders, with visibility into product and strategic decisions.

Compensation and Benefits

Competitive base salary and bonus.

Equity participation.

401(k).

Medical, dental, and vision coverage.

Flexible work environment.

Interested?

If you enjoy turning complex data and emerging AI capabilities into reliable production systems, we’d welcome a confidential conversation.

Our client is a venture-backed healthcare technology company helping cancer care teams turn complex clinical information into better-informed treatment decisions. Its platform supports clinical collaboration, care planning, and research, serving healthcare providers and life sciences organizations.

The company is expanding its data and AI capabilities to make clinical information more accessible, reliable, and actionable. This role offers the opportunity to build the systems behind that work while contributing to technical and product decisions in a growing business.