Jobs at SPREEAI

Build and operate scalable AI infrastructure for multimodal model training and inference. Responsibilities include developing model-serving APIs, distributed GPU systems, backend services, deployment automation, observability, reliability improvements, and performance and cost optimization. The engineer will partner with applied science teams to productionize generative and vision models while improving platform usability and scalability.

Lead the design and operation of SPREEAI’s ML platform, including training workflows, model deployment, inference serving, GPU infrastructure, observability, evaluation, and production monitoring. Establish reliable research-to-production pipelines, model governance, SLOs, rollback strategies, and performance optimization across distributed systems. Partner with research, product, and engineering teams to deliver scalable, cost-efficient multimodal AI systems for real-time virtual try-on experiences.

Build and maintain React interfaces and own the Go or Node.js backend services supporting partner-facing applications. Design REST APIs, authentication, webhooks, and database systems while ensuring performance, accessibility, testing, and polished user experiences. Collaborate directly with product and partner engineering leads to deliver features end to end, and help establish engineering practices as the team scales.

Develop multimodal AI systems for image, video, generative AI, and virtual try-on applications. Optimize diffusion models for controllability and efficiency, design visual evaluation frameworks, productionize research prototypes, and collaborate with infrastructure teams to improve model training and inference.

Build and maintain React.js interfaces across SPREEAI’s Partner Portal, Shopify onboarding flow, demo site, and Rescale App. Collaborate with backend engineers on API contracts, own frontend performance and accessibility, translate onboarding requirements into shipped features, and help establish engineering practices, testing standards, and component patterns. The role requires strong frontend ownership, polished customer-facing experiences, and effective communication in a fast-moving environment.

Own the end-to-end ML lifecycle platform, including training-as-a-service, experiment tracking, model CI/CD, data versioning, monitoring, and cost governance. Build infrastructure for multi-GPU training, automated evaluation gates, canary releases, A/B testing, and checkpoint management. Partner with ML Scientists to improve reproducibility and workflow efficiency, integrate external model providers, and help guide platform direction while mentoring engineers.

Design, build, and operate backend services for partner-facing APIs and internal ML tooling. Own webhook delivery, authentication, API versioning, pagination, idempotency, database schema and query design, and production reliability. Collaborate with AI Platform and Partner Engineering on inference-serving and partner integrations. Help establish backend engineering practices, service patterns, and code review standards in a fast-moving startup environment.

Supports partnership prospecting, high-volume outreach, CRM management, partner account communications, onboarding, demo creation, quality assurance, event execution, and cross-functional partnership initiatives. The role manages pipeline reporting, coordinates with product and engineering teams, and helps drive partner acquisition and revenue growth in a remote startup environment.