Backend Software Engineer – Matching and Scheduling Optimization

Sage Care Inc • Palo Alto, CA

71d • Hybrid

About The Position

At Sage Care, we’re transforming healthcare with AI-powered solutions that streamline care navigation and optimize care delivery for health systems. Our technology makes it easier for patients to access the right care, enables providers to focus on the patients who need them most, and improves operational efficiency across the entire system. Built by experts from Carbon Health, Apple, and Uber, our platform automates triage, enhances provider-patient matching, and maximizes appointment and care capacity.
We’re on a mission to make healthcare more accessible, efficient, and equitable for everyone — and optimization engineering is core to that mission.
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.

Requirements

• 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.

Nice To Haves

• 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.

Responsibilities

• 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.
• 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.
• 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.
• 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.

Benefits

• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 1-10 employees