About RapidClaims
RapidClaims is a leader in AI-driven revenue cycle management, transforming how US healthcare providers run mid- and end-revenue cycle operations — from medical coding and charge capture through claim scrubbing, denials management, appeals, and payment posting.
The company has raised $11 million in total funding from top investors, including Accel and Together Fund.
Join us as we scale a cloud-native platform that runs self-hosted, fine-tuned Large Language Models, knowledge graphs, and embedding-based retrieval over millions of clinical notes, claims, and payer-policy documents every month. You’ll engineer autonomous pipelines that parse clinical records and translate into codes, provide documentation improvement parameters, and even solve for denials with autonomous calling if needed; Tackle the deep-domain challenges that make clinical and RCM AI one of the most rewarding problems in tech.
Senior AI/ML Engineer- Job Overview
We are hiring a Senior AI/ML Engineer to own the end-to-end applied LLM, retrieval, and evaluation layer of our healthcare AI platform. You will build production systems that automate mid- and end-revenue cycle workflows for US healthcare spanning coding, claim edits, denials triage, appeal generation, and payer-rule reasoning.
This is a production engineering role (not research) focused on building scalable, auditable, and cost-efficient LLM systems in a regulated healthcare environment
What You’ll Own
- Self-Hosted LLM Infrastructure
- Deploy, fine-tune, and operate open-source models (Llama, Qwen, MedGemma, and
- successors) as our primary inference stack
- Work with vLLM / SGLang / TensorRT-LLM for serving at scale, with disciplined attention to throughput, tail latency, batching, KV-cache, and GPU economics
- Own fine-tuning workflows end-to-end (SFT, LoRA, QLoRA, DPO) on clinical notes, claims, and payer-rule data
- Optimize GPU usage, latency, batching, and cost; make build-vs-buy and hosted-vs-self-hosted trade-offs explicit and measured
2.Knowledge Graphs & Embedding-Based Retrieval
- Design and maintain the knowledge graph encoding ICD-10-CM, CPT, HCPCS, modifiers, HCC, NCCI edits, LCD/NCD policies, and payer-specific rules — and the relationships between them
- Build embedding-based retrieval over clinical notes, historical claims, denial reasons, and payer-policy corpora — including chunking, embedding model selection, hybrid search, and reranking
- Combine graph traversal and dense retrieval so every coded line, scrubbed edit, and appeal response is grounded in auditable evidence
- Own ingestion, versioning, and quality of underlying knowledge sources (CMS, AHA, AMA, NCCI, payer bulletins)
3.Evaluation & Monitoring
- Build continuous evaluation pipelines that gate every model, prompt, retrieval, and graph change before production
- Run offline eval suites grounded in coder- and biller-validated labels; use LLM-as-judge where appropriate, calibrated against human ground truth
- Monitor drift, hallucinations, regressions, and output quality in production; operate shadow-mode rollouts and per-cohort accuracy tracking (specialty, payer, chart type)
- Track business metrics: chart-level and opportunity-level coding accuracy, denial rate impact, clean-claim rate, cost per chart, and end-to-end latency
4.LLM Systems & Prompt Engineering
- Design prompts and context pipelines for coding (CPT, ICD, HCC, E/M), claim edits, denial classification, and appeal drafting
- Implement structured outputs (JSON, function calling, constrained decoding) on top of the self-hosted stack
- Apply RAG over medical coding standards (CMS, ICD-10, AHA, NCCI) and payer policies, grounded in the knowledge graph and embedding stores
- Treat prompts as a thin, well-versioned, well-evaluated layer — never the load-bearing piece
5. Agentic Workflows & Tooling — MCP
- Build MCP servers for internal tools: code lookup, NCCI / rule checks, payer logic, eligibility, denial classification
- Design multi-step agent workflows with audit trails and human-in-the-loop checkpoints for coder, biller, and AR-analyst review
- Define deterministic vs. LLM-based tool boundaries for reliability — reliability comes from knowing which is which
What We’re Looking For
Must-Have
- 5+ years in ML/AI engineering, including 6+ months in production LLM systems
- Hands-on experience deploying and operating self-hosted LLMs (vLLM, SGLang, TensorRT-LLM, or equivalent)
- Strong experience designing embedding-based retrieval and/or knowledge graphs for grounded LLM applications
- Demonstrated ownership of evaluation infrastructure — offline benchmarks, online monitoring, drift and regression detection
- Strong Python + PyTorch + Hugging Face experience
- Production experience with monitoring, incidents, and system ownership
Strongly Preferred
- Fine-tuning experience (SFT, LoRA, QLoRA, DPO) on domain-specific corpora
- Experience with graph databases (Neo4j, ArangoDB, or equivalent) and graph-aware retrieval
- Experience with vector databases and hybrid search (BM25 + dense, rerankers)
- Familiarity with LLM observability tools (Langfuse, LangSmith, Arize, Braintrust, or in-house equivalents)
- Exposure to healthcare, RCM, claims, or other regulated domains
- Experience with MCP or similar tool-orchestration frameworks
- Strong prompt-engineering and LLM-evaluation instincts
What We Offer
- Work on high-impact healthcare AI systems used in real billing and RCM workflows
- Ownership of production LLM, retrieval, and evaluation systems end-to-end
- Solve real-world problems with real constraints (cost, latency, compliance, auditability)