Staff ML Platform Engineer - Production & Scale

Credit Acceptance Corporation; Southfield (MI); Hybrid; USD 154,000 - 226,000; Full time

Benefits offered by this job

401(K) match

Adoption assistance

Parental leave

Tuition reimbursement

Medical/Dental/Vision

Great Place to Work culture

Job summary

Credit Acceptance Corporation is seeking an experienced ML Platform Engineer to own and operate the production platform for ML and GenAI models. You will manage training and inference pipelines, model registry, serving endpoints, and promotion across environments while collaborating with Cloud, Data, Security, and SRE teams.

This role emphasizes reliability over modeling, with responsibilities for observability, cost analysis, incident response, and scalable infrastructure.

Qualifications

• Bachelor’s degree with 7+ years in technical field; or a Master’s with 5+ years relevant experience.
• 5+ years building/operating production ML/AI systems with end-to-end ownership.
• Strong Python/SQL with production-grade engineering, CI/CD for ML workloads.

Responsibilities

• Own deployment path for ML/GenAI models end-to-end: training pipelines, registry, serving, and promotion.
• Own runtime health for models in production: monitoring, latency, autoscaling, incident response.
• Build observability and evaluation substrate: traces, logs, evaluation pipelines, data contracts.

Skills

Python

SQL

CI/CD

Cloud platforms

AWS

Databricks

LLM/GenAI ops

Agent architectures

Containerization

IaC

Observability

GPU serving

OpenTelemetry

Education

Bachelor's degree in CS/Engineering/Statistics or related field

Master's degree in related field (preferred)

Tools

MLflow

Databricks Unity Catalog

AWS Bedrock

Terraform

Kubernetes

Databricks

Job description

Credit Acceptance Corporation is seeking an experienced ML Platform Engineer to own and operate the production platform for ML and GenAI models. You will manage training and inference pipelines, model registry, serving endpoints, and promotion across environments while collaborating with Cloud, Data, Security, and SRE teams.

This role emphasizes reliability over modeling, with responsibilities for observability, cost analysis, incident response, and scalable infrastructure.