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Staff Data Engineer (AI-Oriented)
Location: Remote
Company: Oncolens
About Oncolens
It is hard to find someone in today's world who hasn't been impacted by cancer. At OncoLens, we envision a world where every cancer patient has access to the best possible minds, therapies, and innovations in care—in time to make a difference. We deliver tech-based solutions for cancer care professionals and life science companies to improve treatment plans and save lives.
OncoLens is the leading data and clinical decision support hub that supports the multi-disciplinary discussion of cancer cases by experts and care teams, clinical trials, and research and tracking for the highest potential quality or outcome for the patient. More than 10% of all cancer centers in the United States count on OncoLens to help improve outcomes through coordinated patient data, in-depth analysis, and actionable clinical decision support
OncoLens is a global, venture-backed healthcare company, serving some of the largest integrated delivery networks and national cancer institutes across the country.
Join our team to get hands-on knowledge of not only the technology that powers OncoLens but also exposure to the day-to-day business and strategy decisions in a rapidly growing company.
The Opportunity
We're hiring a Staff Data Engineer with strong AI orientation to help build and scale our data and AI platform.
This is a hands-on, high-ownership role for someone who can operate across the stack—from data pipelines to production AI systems—and who cares about shipping real, working solutions (not just prototypes).
If you thrive in ambiguity, think like a product owner, and use AI as a force multiplier, this role puts you at the center of high-impact initiatives.
What You'll Do
Build & Own Data Systems
Design and maintain scalable data pipelines (batch and real-time)
Ensure data quality, consistency, and reliability across systems
Eliminate gray areas between upstream and downstream data
Ship AI-Driven Features
Partner with product and engineering to deliver AI-enabled functionality
Deploy, monitor, and troubleshoot LLM-based and ML systems in production
Focus on shipping reliable outputs, not just experimentation
Operationalize & Validate
Productionize models and workflows into robust, maintainable systems
Build validation frameworks and sanity checks for output quality
Improve observability, performance, and system reliability
Work Through Ambiguity
Translate loosely defined problems into clear technical solutions
Make pragmatic tradeoffs and clearly communicate decisions
Execute quickly while maintaining a high quality bar
What We're Looking For
Strong experience in data engineering, including:
Building and maintaining pipelines
Experience with Spark or similar distributed systems
Proficiency in Python, SQL, and modern data platforms
Experience operationalizing data systems and/or models in production
Exposure to AI/ML systems, including:
Working with or integrating LLMs or modern AI tools
Understanding of probabilistic systems and LLM fundamentals
Using AI tools to accelerate development and problem solving
Solid statistical intuition:
Ability to reason about data quality, anomalies, and outputs
How You Think
Product-oriented: Focused on outcomes, not tasks Critical thinker: Naturally validates outputs and assumptions Pragmatic: Leverages existing tools vs. over-engineering Adaptable: Comfortable in a fast-moving AI environment
Collaboration & Communication
Clear, proactive communicator with strong follow-through
Able to explain how and why you approach problems
Comfortable working cross-functionally with product, engineering, and data teams
Why Join OncoLens
Build real-world AI systems that directly impact patient outcomes
Work in a fast-moving, high-ownership environment
Gain exposure to both technical and strategic decision-making in a scaling company
Compensation & Benefits
Competitive salary + Bonus
Equity participation
401k
Medical / Dental / Vision
Flexible work environment
Interested?
If you're excited about building production-grade data and AI systems that matter, we'd love to connect.