Senior ML Platform Engineer

Spin Master

. Own the architecture and performance of the machine learning pipeline powering Stickerbox .

Posted 10/7/2026full-timeLong Island City • New York • United StatesSenior💰 $166,100 - $221,400 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in machine learning pipeline architecture, performance optimization, and deployment strategies for consumer hardware. Proficient in managing end-to-end production processes, including latency optimization and compliance for child-directed digital products.

Highest-signal resume keywords

Machine Learning Pipeline ArchitectureGenerative ML Model DeploymentPerformance OptimizationEnd-to-End Production OwnershipConsumer Hardware Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningModel DeploymentLatency OptimizationPerformance ProfilingInference SystemsCapacity PlanningObservabilityOn-Call SupportASRChild-Directed Digital Products

Tools & Technologies

GPU UtilizationBatchingCachingRelease EngineeringAutoscalingObservability Tools

Industry Keywords

Product Safety EnforcementKid-Safe ComplianceAutomated Character-Fidelity ChecksBuild-Vs-Buy AnalysisEdge Inference

About the role

Key responsibilities & impact

Own the architecture and performance of the machine learning pipeline powering Stickerbox
Scale the ML technology to support a global product launch
Make foundational architectural decisions and translate product requirements into reality
Own end-to-end latency from voiced input to physical printed sticker and profile bottlenecks
Design and manage model deployment, versioning, and rollback strategies for consumer hardware devices
Optimize batching, caching, and GPU utilization across image generation, speech recognition, and character models
Conduct build-vs-buy analysis for hosting and inference architecture and lead migrations
Design and maintain serving-side product safety enforcement, kid-safe compliance, and automated character-fidelity checks
Manage queues, autoscaling, observability, and release engineering supporting the ML generation path

Requirements

What you’ll need

Experience deploying and operating diffusion or similar generative ML models in production with real latency constraints
Experience profiling a generation path to optimize for performance and cost
End-to-end production ownership experience, including deployment, capacity planning, observability, and on-call support
Advanced knowledge of ML infrastructure, inference systems, and production software architecture
Experience translating product requirements into scalable technical solutions and roadmaps
Experience with consumer hardware, on-device/edge inference, ASR, or child-directed digital products is a strong plus

Benefits

Comp & perks

Medical, dental and vision coverage
Retirement savings programs
Paid time off
Paid holidays
Wellness resources
Life and disability insurance
Employee assistance programs
Other company-sponsored benefits
Reasonable accommodation for applicants who require provisions to participate in recruitment, selection, or assessment processes
Opportunities for future advancement and internal transfers