Applied AI Engineer/Scientist — Healthcare AI Platform
- Pay Rate: $110.00 – $190.00/hour (on W2)
- Work Type: Hybrid – 3 days Onsite at the company's office hub in a major metro area.
- Location: Austin, TX 78701
- Contract Length: 1+ Years
Raise is currently hiring a contract team member on behalf of our client. They’re expanding their team to meet growing needs, making this a unique opportunity to work with an industry leader.
About the Customer
The customer is a healthcare technology company building an AI-native operating system for healthcare revenue and clinical operations — a connected platform that reasons over medical records, payer logic, and financial workflows to automate medical coding, billing, and follow-up.
The company's agentic AI systems already run in production across a large share of major U.S. health systems, processing hundreds of millions of patient encounters annually, including well over 100M claims, 500M+ patient encounters, and over a billion workflow actions and outcomes each year.
The role offers startup-level ownership with enterprise-level impact, for engineers who want to build AI that ships, scales, and measurably improves how healthcare works.
The Role
The customer is looking for an Applied AI Engineer/Scientist to build, evaluate, and continuously improve clinical AI agents and supervised ML models.
The role sits at the intersection of software engineering, LLM systems, evaluation, model improvement, and healthcare workflow understanding. The engineer's job is to turn frontier model capability into reliable production behavior: agents that read complex medical records, use the right clinical and coding context, call the right tools, produce auditable outputs, and improve from real-world failures.
The person in this role will be embedded in hard healthcare problems, including clinical documentation integrity, medical coding, denial prevention, appeals, revenue cycle workflows, and payer logic, owning the full loop from problem framing to agent design, evaluation, deployment, trace analysis, and ongoing improvement.
The ideal candidate is a strong engineer who thinks like an applied scientist: rigorous about measurement, comfortable with ambiguity, excited by messy real-world data, and motivated by closing the gap between impressive demos and dependable production systems.
Key Responsibilities
- Design, build, and iterate on agentic AI systems for complex healthcare workflows, including documentation, coding, denial management, appeals, and revenue cycle automation
- Develop long-horizon agent behavior across context construction, retrieval, tool use, memory, routing, verification, escalation, and human-in-the-loop review
- Define what “good” looks like for clinical agents end-to-end, translating expert workflows into specifications, rubrics, gold standards, test cases, and clinically meaningful success criteria
- Build rigorous evaluation and feedback loops using expert review, production logs, model outputs, and benchmarks to measure performance, regressions, edge cases, safety, reliability, provenance quality, and business impact
- Prototype new AI capabilities from 0 → 1, then harden them into reliable, explainable, auditable production systems with clear contracts, monitoring, evidence, rationale, and performance gates
- Partner with research and ML engineering teams on model selection, fine-tuning, reward modeling, distillation, synthetic data, post-training, and internal AI infrastructure, including instrumentation, experiment tracking, benchmarking, prompt/version management, and reproducible evaluation
What Makes This Role Different
Most AI roles are either too research-heavy or too product-light. This role sits in the middle. The engineer will not only write prompts or run experiments but will own whether an agent actually works in production. That means understanding the workflow, designing the system, building the evals, inspecting failures, improving the agent, and proving that the improvement matters.
The right person will be motivated by questions such as: What context does the agent need to make the right decision? How does the team know an output is clinically and operationally correct? Which failures are prompt problems, retrieval problems, model problems, tool problems, or product-spec problems? How is expert feedback turned into a better benchmark or training set? When should prompting, RAG, rules, fine-tuning, reward modeling, or a different architecture be used? How are agent outputs made auditable enough for clinical and operational review? How is a data flywheel built that improves the system every week?
Candidate Profile
- 4+ years of software engineering, ML engineering, research engineering, or applied AI experience
- High proficiency in Python and comfort building production systems with APIs, structured data, async workflows, testing, logging, and observability
- Experience turning messy real-world workflows into structured AI problems, including classification, ranking, extraction, decisioning, LLM applications, agents, RAG, tool calling, structured outputs, prompting, or evaluation
- Experience building or operating evaluation systems, benchmarks, annotation workflows, experiment tracking, or regression tests for AI systems
- A track record of thriving in ambiguous, high-stakes domains: working with experts, debugging real-world failures, and turning model potential into reliable, correct, safe systems that work for users
Role Leveling
- Candidates are considered across levels ranging from L2 to Staff:
- L2: Independently delivers a complete end-to-end project, owning design, implementation, and delivery of scoped work
- L3: Leads delivery of larger projects, handling increased technical complexity and ambiguity, and providing light guidance to L2s on shared work
- Senior: Team Lead responsible for managing a portfolio of projects that contribute to major technical initiatives
- Staff: Impact at the organizational level, leading multiple teams or broad initiatives across the organization
Benefits
- Top-of-market compensation (salary + equity)
- Flexible PTO
- Comprehensive health benefits
- 410(k) matching
- A mission-driven team
Looking for meaningful work? We can help
Raise is an established hiring firm with over 65 years of experience. We believe strongly in making the world a better place through work, which is why we’re a certified B Corporation and donate 10% of our profits to charity.
We strive to build teams that reflect the diversity of the communities we work in. We encourage all qualified applicants to apply, including people from traditionally underrepresented groups such as women, visible minorities, Indigenous peoples, people identifying as LGBTQ2SI, veterans, and people with visible/nonvisible disabilities.
We have a dedicated webpage for accommodations where you can learn more about what we offer, and request accommodation: https://raise.jobs/accommodations/
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