MLOps Engineer
At a glance
Mid-level ML Platform & Ops role at SpreeAI. Hybrid (San Francisco, California, US) · full-time · no salary stated · we estimate $144,980–$225,625 across the United States.
SpreeAI has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Build training infrastructure so scientists can start multi-GPU jobs, inspect metrics and receive completion alerts without handling the systems
- Create model release checks that stop poor checkpoints, with canary deployments and hooks for A/B tests
- Manage experiment records and dataset versioning for terabyte-scale data that changes weekly
- Watch GPU use, loss curves and out-of-memory events, then control costs with alerts and flexible compute
- Turn scientists’ workflow problems into platform tools, and connect external model providers to training and evaluation
What you bring
- You have operated an ML platform with a training system such as Ray, Kubeflow or Airflow, along with Docker and Kubernetes
- You can use Python or Go to build pipeline orchestration and infrastructure tools
- You have production-scale experience with experiment tracking and data versioning
Who this fits
This role suits an engineer ready to own the ML platform across the lifecycle and mentor other engineers as the team grows. You’ll work directly with ML Scientists to define the split between platform and research responsibilities. It also calls for setting platform direction and protecting its integrity when evaluating requests.
From the employer
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. Our mission is to redefine the retail landscape with cutting-edge AI solutions that blend high fashion and technology. We thrive in a dynamic, fast-paced environment where creativity meets technology to drive real impact. If you are passionate about innovation and shaping the future of fashion, SPREEAI offers a platform to make your mark.
About the team
AI Platform scales SPREEAI's infrastructure: productionizing ML model checkpoints, running the API services behind the Partner Portal, optimizing inference serving, and giving ML Scientists training-as-a-service so they can iterate without managing infrastructure themselves.
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 you'll do
- 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
What you'll bring
- Experience building or operating an ML platform: training orchestration (Ray, Kubeflow, Airflow, or a custom solution), Docker, Kubernetes (Jobs/CronJobs, Helm)
- Python or Go for pipeline orchestration and infrastructure tooling
- Real experience with experiment tracking and data versioning tools at production scale
- Comfort owning platform direction, not just executing tickets, and mentoring engineers as the team scales
- Comfort with broad ownership across the ML lifecycle in an early-stage, fast-moving environment
You'll thrive here if
You treat ML Scientists as your customers and actively design the boundary between platform responsibility and scientist responsibility rather than letting it happen by accident. You can make the case for a platform investment that isn't obviously urgent yet, and you're comfortable saying no to a feature request that would compromise platform integrity.