Senior Staff Software Engineer, Recognition Platform

Metropolis•Los Angeles, CA

3d•$232,000 - $268,000•Onsite

About The Position

Metropolis is seeking a Senior Staff Software Engineer to serve as the technical owner of the Metropolis Recognition Platform (MRP). This role owns the platform’s architecture and leads its evolution across application engineering, computer vision, data, security, platform teams, and external partners. The engineer will set technical direction, establish architectural RFCs, and drive cross-functional alignment without formal reporting authority. The position focuses on high-volume edge-to-cloud pipelines and decisioning systems that transform physical-world sensor signals into real-time actions. Responsibilities span edge ingestion, identity resolution, decisioning, notifications, and operator interfaces, with an emphasis on reliability, latency, privacy, and production quality. The role follows an office-first model requiring at least four days per week onsite.

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.
Comfort navigating physical-world deployment constraints, hardware conditions, and operational complexity beyond staging environments.
Deep understanding of observability, automated testing, and progressive delivery.
Fluency with AI-assisted engineering tools such as Claude Code and GitHub Copilot in daily workflows.

Nice To Haves

Experience with computer vision, biometric matching, or sensor-fusion systems, particularly 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; drive spec-driven development through RFCs and manage roadmap sequencing.
Drive cross-functional alignment and resolve ownership ambiguities across application, computer vision and machine learning, data, security, and platform engineering teams, as well as external partners.
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 for biometric-regulated environments, including bounded-time opt-out propagation, in 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.