Location: Palo Alto, CA (Hybrid: Tuesday through Thursday onsite)
Sage Care is a fast-growing Series A healthcare technology startup founded by leaders from Apple, Uber, and Carbon Health. We recently emerged from stealth with $20 million in funding led by Yosemite, and investors including General Catalyst, Metrodora Ventures (co-founded by Chelsea Clinton), OVTR.VC, SV Angel, Liquid 2 Ventures, Seven Stars, Refract Ventures, AME Cloud Ventures, and Apolo Ohno.
Our founding story and vision were recently profiled in Forbes, highlighting Sage Care’s mission to build an “air-traffic-control system for healthcare.”
With a strong customer pipeline, Sage Care is transforming healthcare by simplifying care navigation. Our platform makes it easier for patients to find the right doctor, helps providers focus on those who need them most, and ensures faster access to care. By harnessing clinically grounded AI and real-time optimization, we improve operational efficiency, increase system capacity, and deliver better patient outcomes at scale.
As a Backend Software Engineer focused on Optimization, you will help architect and implement the high-performance backend services that power Sage’s care-logistics and navigation engine. You’ll help translate complex real-world constraints ranging from provider schedules, patient needs, and operational workflows, into well-structured optimization problems that can be solved at scale.
This role blends algorithmic thinking with production-level engineering. You’ll work closely with engineers, data scientists, and product teams to translate healthcare workflows into formalized matching and scheduling problems, then build the backend services to solve them in real time. Your work will directly impact how health systems match patients to care, increase throughput, reduce bottlenecks, and improve access across large provider networks.
Optimization Design & Implementation
Design and build backend services that support scheduling, matching, and resource-allocation optimization for patient care pathways.
Formalize operational requirements like clinical constraints, time windows, continuity rules, and workforce capacity into parameters for well-structured optimization problems.
Implement high-performance algorithms (e.g., job scheduling algorithms, optimal matching, constraint solving) and integrate them into production services.
Backend Engineering Excellence
Optimize for performance: reduce latency, improve throughput, increase reliability, and ensure consistent SLA adherence.
Build well-structured APIs, data models, and distributed service components that support real-time decisioning.
Profile, benchmark, and tune services to handle the scale and unpredictability of healthcare operations.
Cross-Functional Collaboration
Collaborate with data scientists to transform prototypes into reliable, production-grade services.
Partner with product and infra teams to align technical constraints with business requirements, regulatory needs, and system architecture.
Monitoring, Reliability & Continuous Improvement
Help build observability into all systems with a strong attention to reliability.
Continuously refine algorithms and system design based on new data sources, operational feedback, and product expansion.
Bachelor’s or Master’s degree in Computer Science, Engineering, Operations Research, or a related field.
5+ years of experience in backend or systems engineering in production environments.
Strong experience with Java or another high level programming language (e.g. Python, Go, C++, or Rust).
Demonstrated experience building low-latency, high-throughput, and high-reliability backend systems.
Familiarity with cloud environments (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
Excellent communication skills and ability to collaborate with cross-functional teams in a domain-heavy environment.
Experience with combinatorial optimization and constraint-solving algorithms using optimal dispatch, routing, linear and mixed integer linear programming, or metaheursitic algorithms (e.g. evolutionary algorithms).
Familiarity with optimization libraries such as OR-Tools, Gurobi, CPLEX, or similar tools.
Background in logistics, scheduling, workforce optimization, or healthcare operations.
Experience with real-time systems where reliability, interpretability, and resilience are critical.
Work on deeply meaningful, high-impact problems that directly improve patient access and provider efficiency.
Join a high-performing engineering team where ownership is large and impact is immediate.
Competitive compensation, meaningful equity, and comprehensive benefits.
Hybrid work culture with deep collaboration across engineering, product, and clinical teams.
The opportunity to define the core optimization engine that powers the future of healthcare operations.
Our interview process typically includes:
Initial review and a 30–60 minute take-home technical assessment
Recruiter conversation
Hiring Manager and/or Engineer chats
Onsite interview loop (including a team lunch!)
Reference check and offer
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