ML Ops Engineer

Rise Technical Recruitment Limited; San Francisco (CA); Hybrid; USD 140,000 - 180,000; Full time

Benefits offered by this job

Equity

Healthcare

401(k)

PTO

Job summary

Rise Technical Recruitment Limited is seeking an experienced ML Ops Engineer for a hybrid role in San Francisco, CA. You will own end-to-end machine learning infrastructure, build multi-GPU training-as-a-service, and deploy automated CI/CD for models, while optimizing GPU utilization and cost governance.

You will collaborate with research and engineering teams to scale training pipelines, experiment tracking, and data versioning, enabling scientists to launch and iterate rapidly in a fast-paced

Qualifications

• Experience with Ray, Kubeflow or Airflow with Kubernetes.
• Strong Python or Go development skills.
• Production-scale experiment tracking experience.
• Background in GPU monitoring and cost optimization.
• US Citizen or Green Card Holder.

Responsibilities

• Build multi-GPU training-as-a-service infrastructure.
• Deploy automated model CI/CD and evaluation gates.
• Manage experiment tracking and data versioning.
• Optimize GPU utilization and cost governance.

Skills

Python

Go

Experiment tracking

GPU monitoring

Cost optimization

Tools

Ray

Kubeflow

Airflow

Kubernetes

Job description

Salary: USD140000 - USD180000 per annum + 401K + Healthcare + PTO + Equity

ML Ops Engineer

San Francisco, California (Hybrid)

$140,000 - $180,000 + Equity + Healthcare + 401(k) + PTO

Are you an MLOps Engineer looking to take full ownership of end-to-end machine learning infrastructure and scaling platforms for a high-growth AI company transforming the digital commerce space?

This is an opportunity to join a fast-growing, innovative AI company at the forefront of the retail technology sector, revolutionizing how consumers engage with products through advanced visual generation and hyper-personalized shopping experiences. As the science team expands into new video and sizing capabilities, you will join the core AI Platform team to build and scale training-as-a-service infrastructure, automated model CI/CD, and inference serving.

In this role you will step up to drive the machine learning lifecycle platform end to end spanning training pipelines experiment tracking data versioning and cost governance so machine learning scientists can launch monitor and iterate rapidly without managing infrastructure directly. You will collaborate closely with research and engineering teams to design robust platform abstractions evaluate model service providers and ensure high reliability as workloads scale into the terabytes.

This role would suit an engineer who enjoys treating scientists as customers building scalable internal platforms and moving quickly in an innovative fast-paced environment.

The Role:

• Build multi-GPU training-as-a-service infrastructure
• Deploy automated model CI/CD and evaluation gates
• Manage experiment tracking and data versioning
• Optimize GPU utilization and cost governance

The Person:

• Experience with Ray Kubeflow or Airflow with Kubernetes
• Strong Python or Go development skills
• Production-scale experiment tracking experience
• Background in GPU monitoring and cost optimization
• US Citizen or Green Card Holder

Reference Number: BBBH279595