# Applied AI Engineer at OpenLoop > Remote · United States · Unspecified · ML / AI · open. Source: https://alderhq.app/jobs/ashby%3Aopenloophealth%3A6aa1ee73-a9d9-4e25-a905-a228e8cece5a (read 2026-11-07). ## Facts (observed) - Company: [OpenLoop](https://alderhq.app/companies/openloop) - Location text: United States - Remote - Where it can be done from (inferred): Remote · United States (evidence: "Remote role open worldwide") - Posted: 2026-10-05 (32 days ago) - First seen by alder: 2026-11-02 · last verified: 2026-11-04 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.ashbyhq.com/openloophealth/6aa1ee73-a9d9-4e25-a905-a228e8cece5a - Skills listed (inferred): GCP, LLMs ## Pay and sponsorship - Posted pay: not posted - Market band for similar posted roles (United States, n 735): p25 $183k, median $224k, p75 $275k ## Description (the employer's text) ### About OpenLoop OpenLoop was co-founded by CEO, Dr. Jon Lensing, and COO, Christian Williams, with the vision to bring care anywhere. Our telehealth support solutions are thoughtfully designed to streamline and simplify go-to-market care delivery for companies offering meaningful virtual support to patients across an expansive array of specialties, in all 50 states. ### About the Role OpenLoop is building the future of healthcare infrastructure. We are hiring for this role at the Senior, Staff and Senior Staff levels. Apply once; we will calibrate level during the process based on the scope you have carried and the depth you bring, not on years alone. ### What You'll Do Ship Production Agents: Build and run LLM-based agents and assistants that real users depend on, not prototypes. Agent Runtime & Orchestration: Build how agents run, call tools, hold context and hand off work, including failure handling, retries, timeouts and sandboxing. Evaluation: Build evaluation sets, test harnesses, regression tests and AI-graded evals so the team can show "this got better" with numbers and catch regressions before users do. Model Access & Operations: Work across multiple model providers, manage version upgrades and plan fallbacks for when a provider fails. Observability & Cost: Trace what agents did and why, monitor behavior in production, and attribute AI spend to each agent and use case. Grounding & Retrieval: Connect agents to real, messy company data through retrieval (RAG), context design and prompt engineering, so answers are based on facts. Workflow Design: Work with operations teams to decide which steps of a human workflow an agent can do reliably, and when it should escalate to a person. Security, Privacy & Safety: Keep patient data (PHI) protected in every AI system, control what agents can access, and defend against misuse such as prompt injection. Cloud & Data Foundations: Build on GCP and partner with the Data Platform teams who provide the governed data agents use. Raise the Bar: Contribute to code and design review, and help engineers who are newer to AI. Cross-functional Work: Explain AI trade-offs in plain language to product, operations and clinical stakeholders, including when the honest answer is "the AI isn't ready for this yet." Other duties as assigned. ### Who You Are ### Required Qualifications Bachelor's degree in Computer Science, Engineering, or a related technical field Proven Engineering Experience: 5 to 15+ years of software engineering experience. Production LLM Experience: Hands-on experience building and running LLM-based systems in production that real users relied on, not only demos, prototypes or personal projects. Senior Staff candidates typically bring 2+ years. Core AI Depth: Real hands-on depth in at least one core area: agent runtime, evaluation, retrieval, or observability and cost. At Staff and Senior Staff we look for depth in agent runtime or evaluation, and at Senior Staff a second deep area plus working knowledge across the rest. Evaluation Rigor: Experience measuring whether an AI system works with evaluation data, rather than "it seemed to work." Engineering Fundamentals: Strong production engineering across testing, debugging, services, APIs, deployment and on-call. Data & Security Fluency: Experience handling sensitive data with a security-first mindset. Comfort working in a greenfield space, and clear communication with technical and non-technical partners ### Preferred Qualifications Regulated-Industry Experience: Healthcare (PHI, HIPAA), finance or insurance. GCP Experience: Running production services on Google Cloud. Model Operations: Experience migrating a system between model versions or providers. Classic ML Background: Machine learning experience beyond LLMs. Internal Platforms: Built a platform used by other engineering teams. Team Leadership: For Senior Staff, experience leading a small team's technical direction from zero and growing a single pod into multiple teams. ### Our Benefits In addition, for salaried positions you would also be eligible for: --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Aopenloophealth%3A6aa1ee73-a9d9-4e25-a905-a228e8cece5a and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.