Staff ML Platform Engineer – Production & Infra

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

Benefits offered by this job

Great culture

Casual work environment

Job summary

Credit Acceptance Corporation is seeking an experienced ML platform engineer to own and operate the production ML/GenAI platform and its end-to-end deployment, monitoring, and governance. You will partner with Cloud, Data, Security and SRE to ensure reliable, observable and cost-effective infrastructure for models in production.

The role emphasizes running in a remote-capable environment with occasional travel to a Southfield, MI office.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Statistics or a relevant technical field with at least 7 years of relevant experience.
• 5+ years building and operating production ML or AI systems, with direct ownership of at least two of: training or inference pipelines, model serving infrastructure, model registry and versioning, or production monitoring and alerting.
• Strong Python and SQL, with production-quality engineering practice: version control, testing, code review, and CI/CD applied to ML workloads.
• Hands-on experience with a cloud ML platform in production. AWS and Databricks strongly preferred, including model serving, job orchestration, and a model registry or experiment tracking system such as MLflow.

Responsibilities

• Own the deployment path for ML and GenAI models end to end: training and inference pipelines, model registry and versioning, serving endpoints, and controlled promotion across development, QA and production.
• Own runtime health for models in production: monitoring, alerting, drift and quality-regression detection, latency and throughput objectives, capacity and autoscaling behavior, and incident response through to root cause and a closed corrective action.
• Operate the agent runtime layer. Route production agents through the enterprise AI Gateway and MCP Gateway rather than direct model and tool access, migrate existing agents onto that governed path, and keep tool surfaces scoped, versioned and least-privilege as they change.
• Own evaluation of agents in production, not just before release. Online scoring and behavioral monitoring, quality-regression and drift detection against pinned baselines, sampling and judge pipelines, and the release-gate mechanics that stop a regression from shipping.
• Build and maintain the observability and evaluation substrate other teams depend on: trace and telemetry capture including multi-turn and multi-step agent traces, logging standards, evaluation pipeline plumbing, and the data contracts underneath them.
• Own platform unit economics. Measure and manage cost per inference, per document and per interaction, and produce the platform and infrastructure cost analysis that informs build-versus-buy and hosting decisions.
• Make the paved road real. Deliver reusable pipeline templates, deployment patterns, reference implementations and internal tooling so product teams adopt the standard path because it is faster, not because it is mandated.
• Partner with Cloud Engineering, Data Engineering, Security and SRE so the ML platform sits inside enterprise governance, identity and observability rather than beside it.
• Respond to AI-specific production incidents and drive them to a closed corrective action: prompt injection attempts, rogue-agent cost spikes, data-classification exposure through a tool call, delegation abuse between agents, and model endpoint failures.
• Maintain the architecture documentation and system diagrams for the ML platform, and keep them accurate enough to be used in design review.
• Mentor engineers and interns on production ML practice, and raise the operating standard through design and code review rather than through rework.

Skills

Python

SQL

CI/CD

Cloud platforms

Education

Bachelor's degree in Computer Science, Engineering, Statistics or a relevant technical field

Master's degree (preferred)

Tools

AWS

Databricks

MLflow

Docker

Kubernetes

Job description

Credit Acceptance Corporation is seeking an experienced ML platform engineer to own and operate the production ML/GenAI platform and its end-to-end deployment, monitoring, and governance. You will partner with Cloud, Data, Security and SRE to ensure reliable, observable and cost-effective infrastructure for models in production.

The role emphasizes running in a remote-capable environment with occasional travel to a Southfield, MI office.