Applied AI Engineer

Cadence Solutions

14d•$135,000 - $165,000•Remote

About The Position

Cadence is a clinical AI company that delivers continuous, proactive care for older adults with chronic conditions like hypertension, heart failure, and diabetes. We pair patients with a dedicated clinical team, integrate deeply into health system EMRs and workflows, and use our Clinical Intelligence platform to monitor vitals, surface risk early, optimize medications, and close care gaps between visits. The result: patients engage with care 100x more than before Cadence, clinicians focus on judgment instead of administrative work, and Medicare saves $2M a week. We operate as a full clinical care delivery organization, not a software vendor. Our clinicians work alongside health system partners, extending the reach of local primary care providers into patients' homes. We're now applying AI agents across these workflows – from alert review and medication titration to lifestyle coaching and care coordination – with clinicians always in control of clinical decisions. We're hiring an Applied AI Engineer to design and ship production agents that synthesize real-time clinical data, surface proactive care recommendations, and take action on behalf of clinicians. You will take agent architectures from prototype to production in a domain where reliability directly affects patient outcomes. You'll own the full lifecycle of AI-powered clinical workflows: retrieval, reasoning, tool use, evaluation, and safety guardrails. If you've built agentic systems at scale and want your work to matter beyond engagement metrics, we’d love to speak with you.

Requirements

Bachelor's or Master’s degree in Computer Science, Engineering or related field, or equivalent work experience
3+ years of software engineering experience, with 2+ years building AI/ML-powered systems in production
Experience in a high-growth, fast-paced environment with end-to-end ownership from design through production
Hands-on experience with LLM APIs (OpenAI, Anthropic, open-source models) including prompt engineering, tool use / function calling, and structured outputs
Experience building RAG systems: embeddings, vector stores, retrieval optimization, and grounding
Experience with agent frameworks or orchestration patterns (tool calling, planners, multi-agent coordination) preferred
Fine-tuning experience (SFT, RLHF, LoRA) on domain-specific tasks preferred
Healthcare or regulated-industry experience (HIPAA, SOC 2, clinical data handling) preferred

Responsibilities

Design, build, and deploy clinical AI agents that reason over patient context, invoke tools, and generate care recommendations.
Own reliability, observability, and cost efficiency of LLM-powered workflows at scale.
Build and optimize RAG pipelines over clinical knowledge bases, treatment protocols, and real-time patient data.
Develop evaluation frameworks: offline benchmarks, safety tests, regression suites, and LLM-as-judge pipelines wired into CI/CD.
Design multi-step agent orchestration: planning, memory, tool use, error recovery, and human-in-the-loop escalation paths.
Collaborate with clinical, product, and engineering teams to translate patient care needs into AI system design.
Stay at the forefront of AI and engineering best practices, continuously pushing the team to raise the bar on quality, performance, and architecture.