Position Summary
We're hiring a full-stack .NET engineer who is AI-native — someone who builds production software end to end and who works fluently with modern AI development tooling and agentic workflows.
"AI-native" here means two things, and we care about both:
- You build with AI. You use AI-assisted and agentic development tooling (e.g., Cursor, Claude Code, Copilot) as a core part of how you work — including multi-agent workflows where separate agents handle decomposition, architecture, development, QA, and code review. You know how to drive these tools to ship faster without sacrificing quality.
- You build AI into products. You're comfortable integrating LLMs and ML capabilities into real applications, and you understand the production and safety constraints that come with doing that in healthcare.
The bulk of this role is full-stack .NET product engineering. A meaningful and growing portion involves designing and shipping AI-powered features across our platform. This is hands-on building role, not a research role — we're looking for someone who has built, integrated, and shipped software that users depend on, and who reaches for AI tooling instinctively to do it well.
You will:
- Design and ship AI-driven features across our ophthalmology platform
- Work with third-party LLM integrations
- Develop custom ML models
- Build domain-specific enhancements using clinical data
This is not a research role. We are looking for someone who has built, integrated, evaluated, and deployed AI systems in production environments.
What You’ll Do:
- Design, build, and maintain full-stack features across our ophthalmology platform — backend services and APIs in .NET/C# (.NET Core, .NET 10) and responsive frontend interfaces in HTML/CSS
- Use AI-native and agentic development workflows (e.g., Cursor multi-agent pipelines for decomposition, architecture, implementation, QA, and review) to move quickly while keeping code clean and reliable
- Design and integrate AI-powered features — including third-party LLM integrations — that fit safely into clinical software
- Troubleshoot and improve distributed systems
- Establish and participate in code review processes
- Work within an agile framework, contributing to:
- Sprint planning
- Daily standups
- Retrospectives
- Write clean, maintainable, testable code, and establish and participate in code review
- Collaborate across product, clinical, and engineering teams
- Contribute to architectural decisions, including where and how AI fits into our systems
- Work within an agile framework: sprint planning, daily standups, and retrospectives AI Responsibilities
Depending on the work in front of us, you may:
- Integrate third-party LLMs (OpenAI, Anthropic, Azure OpenAI, Hugging Face) into production features
- Build retrieval pipelines using embeddings and vector databases (Pinecone, FAISS, Weaviate) and implement semantic search
- Develop prompt engineering strategies with testing and versioning
- Design evaluation approaches that measure accuracy, reliability, hallucination rates, and clinical relevance
- Collaborate on deploying, monitoring, and maintaining AI services in production
- Optimize AI feature performance, latency, and cost
Required Qualifications:
- 3–7 years of professional software development experience with strong full-stack delivery
- Strong, current experience with .NET/C# (.NET Core and/or .NET 10)
- Proficiency building responsive UIs with HTML/CSS
- Demonstrated fluency with AI-assisted and agentic development tooling (e.g., Cursor, Claude Code, Copilot), ideally including multi-agent development workflows
- Experience integrating LLM APIs into real, shipped systems (does not need to be the bulk of your career — but you've done it and understand the tradeoffs)
- Solid foundation in data structures, algorithms, API design, and distributed system design
- Experience with at least one major cloud platform (AWS, Azure, or GCP)
- Familiarity with model limitations, evaluation tradeoffs, and the realities of running AI in production
Team & Process Experience:
Experience working in collaborative environments with exposure to:
- Git version control and branching strategies
- Agile methodologies (Scrum/Kanban)
- Task/story management tools
- Code reviews
- Architectural discussions
- Cross-functional collaboration
Mindset & Collaboration:
- AI-native mindset (data, models, feedback loops, iteration)
- Pragmatic builder who understands production constraints
- Comfortable with ambiguity in emerging AI spaces
- Strong communicator, especially explaining AI tradeoffs
- Motivated to apply AI in healthcare where safety and reliability matter
Nice to Have:
- Experience with Blazor, particularly Blazor Hybrid and .NET MAUI for building cross-platform applications on a shared .NET codebase
- Experience building or fine-tuning ML models (NLP, structured data, computer vision)
- Enhancing foundation models with RAG, fine-tuning, embeddings, or adapters
- Computer vision experience, especially medical imaging
- Experience with clinical or regulated datasets; HIPAA familiarity
- MLOps experience: model versioning, experiment tracking, monitoring, CI/CD for ML
- Designing AI evaluation benchmarks
- Reinforcement learning or simulation experimentation (Gymnasium a plus)
- OAuth and systems integration patterns; RESTful API design
- Git branching strategies and experience in Scrum/Kanban environments
What We Offer:
- A core role building real software that eye care practices depend on every day
- The chance to work AI-native — both shipping AI-powered features and using cutting-edge agentic development tooling as part of how we build
- Ownership over meaningful initiatives and a voice in architectural decisions
- A collaborative team at an exciting inflection point
- Professional development opportunities
- Challenging, mission-driven problems in healthcare