Data Engineer (AI)

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.

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.

• 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

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

• 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

• 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

• Competitive salary + Bonus
• Equity participation
• 401k
• Medical / Dental / Vision
• Flexible work environment

If you're excited about building production-grade data and AI systems that matter, we'd love to connect.

Originally posted on Himalayas