About the Role
DexCare is looking for a Senior Machine Learning Engineer to build the AI systems behind our products. Your primary focus will be Optimize AI, which ingests, structures, and governs complex healthcare scheduling rules and institutional knowledge into a single, verified source of truth. The core problem is turning large volumes of messy, inconsistent, and constantly changing operational knowledge into structured knowledge that people and AI agents can rely on to make decisions.
This is a hands-on, mid-level role. You will build, ship, and own production ML and LLM systems, working closely with product, engineering, and data teams. Our AI products share a common foundation, so you will also work across them, including Mila, our patient-facing agents, as priorities shift. You will join a well-established team early in the build-out of DexCare’s AI capabilities, with large health systems already using our products. If you enjoy taking ML systems from prototype to production and making them accurate, reliable, and fast, we’d love to hear from you.
What You’ll Do
- Build systems that ingest unstructured and inconsistent source material, such as policies, protocols, rules, and spreadsheets, and turn it into structured, versioned, and verified knowledge.
- Build extraction, normalization, and validation pipelines using LLMs and classical techniques; detect conflicts, gaps, and stale information, and route uncertain cases to human review.
- Build and optimize Retrieval-Augmented Generation (RAG) pipelines that give downstream systems and agents accurate, grounded, and traceable answers with low latency.
- Build, ship, and maintain components of the agentic systems that act on this knowledge, including orchestration, tool calling, and state management.
- Build evaluation for the systems you ship: ground-truth datasets, accuracy metrics, regression tests that run before every release, LLM-as-judge and human review, and monitoring of quality in production.
- Diagnose and improve accuracy, latency, reliability, and cost of ML systems in production.
- Build components that can be reused across DexCare’s AI products, and contribute to other products as priorities shift.
- Follow ML lifecycle best practices: versioning of models, prompts, datasets, and knowledge; deployment; monitoring; and retraining.
- Handle patient and customer data in line with HIPAA and DexCare’s privacy and security standards.
What You’ll Bring
- 7-9 years of experience in machine learning or software engineering, including building and running production ML systems.
- Experience building systems that extract, structure, and verify knowledge from unstructured sources, such as documents, policies, or operational data, with measurable accuracy.
- Hands-on experience shipping LLM-based agentic systems to production, including orchestration, tool calling, and handling failures with retries and fallbacks.
- Experience building retrieval systems and improving their accuracy and latency.
- Experience evaluating ML and LLM systems: building eval datasets, defining metrics, and using the results to decide what ships.
- Strong Python and software engineering fundamentals, including data structures, algorithms, testing, code review, and API design.
- Familiarity with MLOps practices and at least one major cloud platform.
- Clear communication and the ability to work across product, engineering, and data teams, including across time zones.
- Comfort working in ambiguous problem spaces where requirements and data evolve.
Preferred
- Experience with agent frameworks such as LangGraph or Google ADK.
- Experience with Azure / AWS and their ML and AI services.
- Experience with knowledge graphs, rules engines, or other structured knowledge representations.
- Experience with predictive ML or optimization for problems such as forecasting, scheduling, ranking, or matching.
- Experience with guardrails, safety classifiers, or red-teaming for LLM applications.
- Experience in healthcare or another regulated industry, including handling PHI under HIPAA.
Skills Required
- 7-9 years of experience in machine learning or software engineering, including production ML systems
- Experience extracting, structuring, and verifying knowledge from unstructured documents, policies, or operational data
- Hands-on experience shipping LLM-based agentic systems to production, including orchestration, tool calling, retries, and fallbacks
- Experience building and improving retrieval systems for accuracy and latency
- Experience evaluating ML and LLM systems using evaluation datasets, metrics, and release decisions
- Strong Python and software engineering fundamentals, including data structures, algorithms, testing, code review, and API design
- Familiarity with MLOps practices and at least one major cloud platform
- Clear communication and ability to collaborate across product, engineering, and data teams
- Ability to work in ambiguous problem spaces with evolving requirements and data
- Experience with agent frameworks such as LangGraph or Google ADK
- Experience with Azure, AWS, and their machine learning and AI services
- Experience with knowledge graphs, rules engines, or structured knowledge representations
- Experience with predictive ML or optimization for forecasting, scheduling, ranking, or matching
- Experience with guardrails, safety classifiers, or red-teaming for LLM applications
- Experience in healthcare or another regulated industry, including handling PHI under HIPAA
What We Do
DexCare is a data-driven intelligence company making access to healthcare easier and better for everyone. We enable patients to get the care they need faster, easier, more affordably, with better results. We enable providers to see more patients, easily and more effectively through different modalities. We enable health systems to utilize resources more efficiently and effectively, lower costs, grow revenue, and build enduring relationships with patients and providers.
Why Work With Us
DexCare is a series B venture-backed healthtech startup backed by leading healthcare investors. DexCare is led by a customer-focused mission-driven team experienced in building successful, innovative, and transformative solutions to healthcare’s biggest problems. Join our team and lend your talent to continue moving the initiatives forward.
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