# MLOps Engineer at SpreeAI

- Company: SpreeAI
- What the company does: SPREEAI is a fast-growing, innovative AI company at the forefront of fashion and e-commerce, revolutionizing how consumers engage with fashion through lifelike photorealistic try-on technology and hyper-personalized shopping experiences.
- Company website: https://spreeai.com
- Type: Startups (AI role)
- Level: Mid level
- Location: Hybrid (San Francisco, California, US)
- Work setup: Hybrid
- Pay: $145K to $180K base salary per year (USD)
- Posted: 2026-09-09
- Apply by: 2026-10-24
- Apply: https://ats.rippling.com/spreeai/jobs/57af1570-e740-4174-a33a-56de7e980c32
- Page: https://www.1752.vc/careers/jobs/spreeai-mlops-engineer/

## About the role

This role owns the ML lifecycle platform end to end: training pipelines, experiment tracking, CI/CD for models, monitoring, and data versioning, so ML Scientists can launch, monitor, and iterate on training runs without managing infrastructure directly. As the Science team expands into Video Try-On and AI Sizing, this platform is what keeps that research moving fast without breaking.

## What they're looking for

- Design and operate training-as-a-service infrastructure: a scientist should be able to launch a multi-GPU training job, track metrics, and get notified on completion without touching infra directly
- Build CI/CD for models: automated eval gates that block a bad checkpoint from reaching production, canary rollout, A/B testing hooks
- Own experiment tracking (Weights & Biases, MLflow, or Neptune) and data versioning (DVC, LakeFS, or Delta Lake) for datasets in the terabytes that change weekly
- Monitor training job health (GPU utilization, loss curves, OOM detection) and drive cost governance (spot instances, preemptible VMs, budget alerts) as training costs scale
- Partner directly with ML Scientists to translate workflow pain points (reproducibility, experiment comparison, checkpoint recovery) into platform abstractions
- Evaluate and integrate external model providers (SPREEAI uses Byteplus and Fireworks.AI as MaaS providers) into the training and eval platform

Tags: Engineering