> Markdown version of [/jobs/ext/140837-ai-engineer](https://www.wearedevelopers.com/jobs/ext/140837-ai-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Engineer - **Company:** Natera - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $106,000.0 - $132,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Computational Biology, Data Mining, Python (Programming Language), Azure Machine Learning, Software Deployment, Software Engineering, Large Language Models, Prompt Engineering, Electronic Medical Records, Kubernetes, Machine Learning Operations - **Published:** May 22, 2026 - **Apply:** https://www.builtincolorado.com/job/ai-engineer/9462320 ## About the Role * 5+ years of software engineering experience, with at least 2 years focused on AI/ML or LLM-powered products in production * Demonstrated ability to ship end-to-end - you've taken AI features or products from idea to production, ideally in a zero-to-one context * Strong full-stack engineering skills - proficiency in Python and modern web frameworks; you can build both the model pipeline and the user-facing product * Production experience with LLMs - fine-tuning, RAG, prompt engineering, agentic architectures, structured extraction, evaluation, and observability * High agency and autonomy - you don't wait for permission, detailed specs, or hand-holding. You unblock yourself, seek out the highest-impact work, and drive it to completion * Excellent communication - you can translate complex AI concepts for clinical and business stakeholders, and articulate a vision for what you're building and why Preferred * Experience in healthcare, biotech, diagnostics, or pharma - especially oncology * Familiarity with clinical workflows, electronic health records, or provider-facing software * Track record of working in regulated environments (HIPAA, FDA, CLIA, CAP) * Background in or comfort with computational biology, genomics, or multimodal data (imaging, sequencing, clinical records) * Experience with AWS infrastructure, Kubernetes, MLflow, or similar ML platform tooling * Prior experience in a high-growth startup or zero-to-one product environment ## Description This is a high-autonomy, high-agency position for a builder who thrives in ambiguity and wants to ship AI products that directly impact cancer care. You'll sit at the intersection of engineering, product, and design, working across all three modalities to ship products to customers. Your work will span two critical domains: 1. AI Products for Providers Design and ship AI-powered experiences for oncologists and clinical teams. This includes exploring conversational AI experiences, building agent-based workflows that help providers navigate managing patient care, and bringing new clinical AI products to market. You'll spend meaningful time with customers to deeply understand their workflows and uncover where AI can create transformative value. 2. Commercializing Natera's AI Foundation Models You'll be the bridge between Natera's AI research lab and the launched product. Our team has developed proprietary models that need to be brought to market and into the hands of clinicians. You'll contribute to training and post-training of these models, build the serving infrastructure, shape the user experience, and own the end-to-end deployments of these products. What You'll Do * Ship zero-to-one AI products end-to-end - from customer discovery and prototyping through production deployment and iteration * Build agentic AI systems - design and implement autonomous and semi-autonomous workflows using LLMs, tool-use, memory, and orchestration * Develop AI tools that improve efficiency across clinical operations, data extraction, manual workflows, and more * Commercialize AI research - partner with the AI lab to take proprietary models from research prototypes and into production * Contribute to model training and post-training - fine-tuning, evaluation, safety testing, and optimization of Natera's proprietary oncology models ## Related Videos - [AI in High-Stakes Industries: Lessons Learned](https://www.wearedevelopers.com/videos/100253-ai-in-high-stakes-industries-lessons-learned) - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [WeAreDevelopers LIVE - Markdown, Liquid and Checkouts](https://www.wearedevelopers.com/videos/1814-wearedevelopers-live-markdown-liquid-and-checkouts) - [Data Science on Software Data](https://www.wearedevelopers.com/videos/162-data-science-on-software-data) - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) - [Instant KAI Sandboxes with vCluster: Multi-Tenant, Multi-Scheduler GPU Sharing](https://www.wearedevelopers.com/videos/100333-instant-kai-sandboxes-with-vcluster-multi-tenant-multi-scheduler-gpu-sharing) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)