Senior Staff Software Engineer, Recognition Platform

Metropolis•Seattle, WA

4d•$231,000 - $267,500•Onsite

About The Position

Metropolis is seeking a Senior Staff Software Engineer, Recognition Platform to serve as the technical owner of the Metropolis Recognition Platform (MRP). You are the engineer other engineers route their hardest architectural questions to, holding the entire platform architecture in your head and navigating cross-functional boundaries across application engineering, computer vision, data, security, and platform teams as well as external partners. In this role, you will lead the evolution of high-volume edge-to-cloud pipelines and decisioning systems that transform physical-world sensor signals into real-time actions, setting technical direction, establishing architectural RFCs, and driving cross-functional alignment without needing formal reporting authority.

Requirements

12+ years of experience building and operating distributed, data-intensive production systems, including deep experience as a multi-service platform technical owner
Proven track record of cross-functional technical leadership and aligning independent engineering teams on shared architecture
Expert proficiency in a JVM language such as Java or Scala, alongside fluency in event-driven and service-oriented design using gRPC/protobuf, streaming, and relational data modeling
Hands-on experience designing high-throughput ingestion pipelines with deep understanding of delivery semantics, idempotency, ordering, backpressure, and tail latency
Experience building rules, eligibility, or decisioning systems where deterministic logic coexists with probabilistic or model-driven signals
Strong systems judgment integrating third-party APIs, SDKs, and vendor constraints
Excellent written technical communication skills with a track record of writing decisive RFCs
Comfortable navigating physical-world deployment constraints, hardware conditions, and operational messiness beyond staging environments
Deep understanding of observability, automated testing, progressive delivery, and fluency with AI-assisted engineering tools like Claude Code and GitHub Copilot in daily workflows

Nice To Haves

Experience with computer vision, biometric matching, or sensor-fusion systems, specifically reasoning about confidence, thresholds, and drift
Background operating in privacy- or biometric-regulated domains such as BIPA, CCPA, or GDPR, or working with consent-management systems
Experience with IoT, edge computing, fleet-scale device telemetry, or MQTT-class transports
Expertise in identity resolution or customer data platforms, stitching multi-system signals into durable profiles
Experience with hospitality, retail, or physical-operations software, including hospitality CRM and PMS integrations
Prior experience in a Uber-tech-lead, TL-of-TLs, or principal-adjacent scope

Responsibilities

Lead end-to-end architecture across edge ingestion, identity resolution, decisioning, notification, and operator surfaces while driving spec-driven development through RFCs and managing roadmap sequencing
Drive cross-functional alignment and resolve ownership ambiguities across application, CV/ML, data, security, and platform engineering teams as well as external partner integrations
Evolve high-volume edge-to-cloud streaming pipelines on a horizontally scalable backbone, engineering for tail latency, intermittent connectivity, backpressure, and out-of-order events
Design rules and decisioning layers for real-time actions including watchlist alerting, arrival routing, eligibility, consent gating, frequency capping, and next-best-action logic
Balance deterministic logic with probabilistic signals by establishing explicit thresholds, quantitative cost reasoning for recognition errors, and replay harnesses for model testing
Architect privacy, identity, and consent models designed for biometric-regulated environments with bounded-time opt-out propagation in close collaboration with Legal and Security
Own production quality by defining SLOs, actionable alerting, runbooks, soak testing, and progressive rollout via feature flags
Elevate technical standards through design reviews, code reviews, mentorship, and AI-native engineering workflows