More jobs:
ML Platform Engineer: Build Scalable ML Infra
Job in
San Francisco, San Francisco County, California, 94199, USA
Listed on 2026-07-03
Listing for:
Faire
Full Time
position Listed on 2026-07-03
Job specializations:
-
IT/Tech
Cloud Computing: Infrastructure & Operations, Data Engineering, Machine Learning/ ML Engineer, SRE/Site Reliability
Job Description & How to Apply Below
Requirements
- 8+ years of experience building production ML or data platforms ,
- A degree (preferably graduate level) in Computer Science, Engineering, Statistics, or a related technical field ,
- Strong hands-on expertise with Databricks, Spark, Delta Lake, and MLflow ,
- Proficiency in Python, SQL, and distributed systems concepts ,
- Experience with cloud platforms and infrastructure-as-code ,
- Solid understanding of MLOps best practices: CI/CD, monitoring, reproducibility, and security ,
- Experience supporting multiple ML teams in a shared platform environment ,
- Are an active owner of orphaned problems and are willing to assimilate whatever knowledge you’re missing to get the job done ,
- Faire uses a modern cloud based tech stack. For this role, you’ll want to be proficient with the following: ,
- Python, SQL, Kotlin ,
- PyTorch, MLFlow ,
- Spark, Kafka, Databricks, Snowflake, Fivetran, Iceberg, Unity Catalog, Datadog, Airflow, Cockroach DB, MySQL ,
- AWS, S3, Sage Maker, Kubernetes, Docker, Git Hub Actions, Terraform ,
- Claude Sonnet 4.5, ChatGPT 5.2
- As a Staff Machine Learning Platform Engineer, you will help design, improve, and operate a scalable ML platform to accelerate model training, deployment, and governance. You are the technical bridge between data science and production engineering. You’ll be joining a small but deeply critical team that scales Faire’s ability to support tens of thousands of local businesses in a constantly narrowing retail landscape ,
- Design and operate ML infrastructure, including work spaces, clusters, jobs, and workflows ,
- Productionize ML workloads using Spark, Delta Lake, MLflow, and Databricks Workflows ,
- Teach data scientists how to utilize our ML platform to advance development from notebook to production for our most critical models ,
- Implement Unity Catalog for data governance, lineage, access control, and secure multi-tenant usage ,
- Build CI/CD pipelines for ML using Terraform and Git-based workflows (e.g., Git Hub Actions) ,
- Optimize performance, reliability, and cost across training and inference workloads ,
- Configure Identity and Access Management (IAM) and Role Based Authentication Controls (RBAC) for sensitive data sets ,
- Establish observability for data quality, model performance, and platform health ,
- Build and maintain ML Platform technical documentation
To View & Apply for jobs on this site that accept applications from your location or country, tap the button below to make a Search.
(If this job is in fact in your jurisdiction, then you may be using a Proxy or VPN to access this site, and to progress further, you should change your connectivity to another mobile device or PC).
(If this job is in fact in your jurisdiction, then you may be using a Proxy or VPN to access this site, and to progress further, you should change your connectivity to another mobile device or PC).
Search for further Jobs Here:
×