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Staff Software Engineer, Caching

Job in New York, New York County, New York, 10261, USA
Listing for: Capital Rx
Full Time position
Listed on 2026-08-22
Job specializations:
  • Software Development
    Cloud Engineer - Software, Backend Developer, Software Engineer, DevOps
Salary/Wage Range or Industry Benchmark: 200800 - 251000 USD Yearly USD 200800.00 251000.00 YEAR
Job Description & How to Apply Below
Location: New York

About Judi Health

Judi Health is an enterprise health technology company providing a comprehensive suite of solutions for employers and health plans, including:

  • Judi Rx
    , a public benefit corporation delivering full-service pharmacy benefit management (PBM) solutions to self-insured employers,
  • Judi Health
    , which offers full-service health benefit management solutions to employers, TPAs, and health plans, and
  • Judi
    , the industry's leading proprietary Enterprise Health Platform (EHP), which consolidates all claim administration-related workflows in one scalable, secure platform.

Together with our clients, we're rebuilding trust in healthcare in the U.S. and deploying the infrastructure we need for the care we deserve. To learn more, visit (Use the "Apply for this Job" box below)..

Hybrid 3 days (offices in NYC, Denver, CO and Charlotte, NC area)

Position Summary:

Judi Health is building the technology infrastructure our nation needs to deliver the healthcare we deserve. The Architecture organization is responsible for the technical foundation the rest of the company builds on: the standards, patterns, and infrastructure that engineering teams use to move fast and build efficiently. The Caching team sits within Architecture's Core Platform function, owning the data access layer across the entire platform.

As a Staff Engineer in Architecture, your influence extends beyond what you ship directly. The standards you set, the patterns you establish, and the decisions you document become the reference point for how caching is approached across the organization.

Claims processing, eligibility, accumulations, and prior authorizations drive highly interconnected, high-volume workflows where maintaining a consistent view of data across the platform is a significant architectural challenge. Decisions around caching, invalidation, and state propagation directly impact both performance and correctness, particularly where the cost of a stale or incorrect read is high. You will be directly responsible for defining consistency contracts, designing invalidation patterns, and balancing correctness and performance across a domain with varying tolerance for stale data.

Position Responsibilities:
  • Design, build, and operate the caching infrastructure that serves as the data access layer for one of the most complex domain models in American healthcare
  • Own the full lifecycle of cache design, from eviction policy and service topology to invalidation architecture and operational recovery
  • Define and own the performance and correctness contracts between the caching layer and the services that depend on it
  • Drive adoption of caching best practices across engineering, eliminatinganti-patterns,building shared libraries, and making cache-aware design the default
  • Diagnose and resolve correctness, performance, and stability issues in the caching layer, including leading incident response and post-incident reviews
  • Set the technical bar for the caching team through design review, code review, and direct mentorship of senior engineers
  • Participate in a 24/7 on-call rotation to provide continuous operational support and rapid incident response
Required Qualifications:
  • 10+ years of production engineering experience, with deep specialization in distributed systems and caching infrastructure
  • Production experience operating distributed caching systems, such as Redis/Valkey or, Memcached, at cloud scale, including cluster topology, eviction policy, replication, and failure recovery
  • Experience with multi-tenancy and the operational complexity of running shared caching infrastructure across many independent workloads
  • Deep understanding of cache consistency and the failure modes that emerge at scale, such as thundering herd, hot key saturation, cache leasing, and dual-write consistency problems, and the judgment to choose the right invalidation strategy for a given workload
  • Fluency with cache replacement algorithms beyond LRU (e.g., LFU, ARC, and probabilistic eviction) and the tradeoffs of multi-tier architectures when an in-process layer belongs alongside a shared distributed cache
  • Experience in cloud-native environments where redundancy, resource efficiency, and resiliency are…
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