Backend Platform Engineer — AI-Driven Revenue Workflows

Rox; San Francisco, Northern (CA, KY); Hybrid; USD 150,000 - 210,000; Full time

Benefits offered by this job

Equity

Health Insurance

Job summary

Rox, a San Francisco-based company, seeks a Backend Platform Engineer to architect AI-driven, agentic workflows that empower top revenue teams. You’ll influence the lifecycle from model training to production, collaborating with engineers, designers, and customers to deliver reliable features.

You will design multi-step processes, apply LLM research in production, and lead rapid experimentation in a fast-moving product-focused setting.

Qualifications

• BS or MS in Computer Science or related field. 5+ years of work experience; having built and scaled platform systems is a plus
• Tech stack: Python, Flask, AWS
• Understanding of LLM models and/or deep understanding of services in multiple clouds is a plus
• Be located in or willing to relocate to San Francisco

Responsibilities

• High Ownership: End-to-end design and implement scalable systems independently
• Customers First: Engage with customers to understand requests and ship solutions with product and AI engineers
• Systems Understanding: Knowledge of data storage (Aurora, DynamoDB, Snowflake, S3) and queuing (SQS, Kafka, Spark)
• Operations: Ensure minimal downtime and impact to customers
• Bias for action: Work quickly with weekly deliverables in a fast-paced environment
• Be Curious: Learn, work hard and grow

Skills

Python

Flask

AWS

Education

BS or MS in Computer Science or related field

Tools

Amazon Aurora

DynamoDB

Snowflake

S3

SQS

Kafka

Spark

Job description

Rox, a San Francisco-based company, seeks a Backend Platform Engineer to architect AI-driven, agentic workflows that empower top revenue teams. You’ll influence the lifecycle from model training to production, collaborating with engineers, designers, and customers to deliver reliable features.

You will design multi-step processes, apply LLM research in production, and lead rapid experimentation in a fast-moving product-focused setting.