Role overview
We're seeking a Senior ML Engineer to build next-generation AI systems that help millions of patients access care faster. You'll architect production ML infrastructure handling thousands of hours of service interactions daily in a highly regulated healthcare environment. This is a high-impact individual contributor role—ideal for someone eager to “own the outcome” and push the boundaries of “high tech + high touch” care experiences.
What you'll work on
Architect and build large-scale AI systems that integrate high-volume voice, text, and contextual event streams with extensive knowledge bases to deliver real-time recommendations, automations, and decision support.
Design and operate workflow-oriented AI systems, including DAG-based execution graphs, stateful pipelines, and agent-driven workflows with clear observability, reproducibility, and fault tolerance.
Build agent architectures spanning agent-to-agent coordination, feedback loops, tool-calling systems, and long-running autonomous workflows, balancing control, safety, and adaptability.
Design and implement data models, feature pipelines, and APIs to support model training, low-latency inference, and continuous learning.
Develop predictive, real-time analytics systems that combine streaming data, ML inference, and event-driven triggers to surface insights and automate actions at scale.
Implement and maintain end-to-end ML platforms, including model training, evaluation, deployment, online inference, monitoring, and drift detection.
Partner closely with product managers, data scientists, and QA engineers to translate experimental models into reliable, production-grade AI services.
Identify, diagnose, and resolve performance and scaling bottlenecks across data pipelines, inference services, and orchestration layers as production workloads grow.
What we're looking for
- Experience building AI systems in healthcare or regulated environments, with familiarity with standards such as HIPAA, GDPR, or FDA guidance.
- Proven experience leading complex technical initiatives and mentoring junior engineers.
- Strong applied knowledge of event-driven architectures and streaming systems (Kafka, Pub/Sub, Kinesis, RabbitMQ).
- Hands-on experience designing and operating vector search, RAG pipelines, and hybrid retrieval systems.
- Experience with agent frameworks, multi-agent coordination patterns, and long-running agent loops in production environments.
Familiarity with real-time analytics stacks combining streaming data, ML inference, and operational dashboards.