Staff Machine Learning Engineer, Platform (MLOPS)
Credit Acceptance1 day ago
Remote, United StatesStaff+
Base Salary
$154k - $226k/yr
Responsibilities
- Own ML and GenAI deployment pipelines, model serving, registries, versioning, and promotion across development, QA, and production.
- Operate production model runtime health through monitoring, alerting, drift detection, quality-regression detection, capacity management, autoscaling, and incident response.
- Operate the agent runtime layer through the enterprise AI Gateway and MCP Gateway with governed, scoped, versioned, and least-privilege tool access.
- Own production agent evaluation, including online scoring, behavioral monitoring, sampling, judge pipelines, baselines, and release gates.
- Build observability and evaluation infrastructure for traces, telemetry, logging standards, multi-turn and multi-step agent traces, and supporting data contracts.
- Measure and manage platform costs per inference, document, and interaction and provide cost analysis for build-versus-buy and hosting decisions.
- Create reusable pipeline templates, deployment patterns, reference implementations, and internal tooling for product teams.
- Partner with Cloud Engineering, Data Engineering, Security, and SRE on enterprise governance, identity, and observability.
- Respond to AI-specific production incidents and drive corrective actions to completion.
- Maintain ML platform architecture documentation and mentor engineers and interns through design and code review.
Requirements
- Bachelor’s degree in Computer Science, Engineering, Statistics, or a relevant technical field with at least 7 years of relevant experience, or a master’s degree in one of those fields with at least 5 years of relevant experience.
- At least 5 years building and operating production ML or AI systems, with ownership of at least two major platform areas such as pipelines, model serving, registries, or monitoring.
- Experience owning a production ML or AI service through deployment, monitoring, incident diagnosis, and remediation.
- Strong Python and SQL skills with production engineering practices including version control, testing, code review, and CI/CD for ML workloads.
- Hands-on experience with a cloud ML platform in production and experience with LLM or GenAI operational characteristics.
- Experience running LLM or agent applications behind gateway or proxy layers, including routing, fallback, credential management, rate limiting, and budget enforcement.
- Working knowledge of tool-calling architecture and the Model Context Protocol, including MCP server authorization and gateway-brokered access.
- Experience with containerization and infrastructure as code.
- Ability to communicate technical trade-offs clearly to technical and non-technical audiences.
- Preferred experience includes AWS and Databricks, MLflow, GPU-backed model serving, OpenTelemetry, Dynatrace, model-serving optimization, Databricks Unity Catalog, regulated financial-services AI systems, production agentic systems, managed agent or tool gateways, MCP servers, and Agent2Agent interoperability and identity standards.
Benefits
- Remote work from home with occasional planned travel to or the option to work from the assigned Southfield, Michigan office.
- Competitive base salary range of $154,120 to $226,042 plus eligibility for a 10–20% annual variable cash and equity bonus.
- 401(k) match, adoption assistance, parental leave, tuition reimbursement, comprehensive medical, dental, and vision coverage, and additional nonstandard benefits.
- Professional development, continuous improvement, and a casual work environment.
Categories
About Credit Acceptance
Credit Acceptance is a public auto finance company that partners with a nationwide network of car dealers to provide indirect vehicle loans to consumers, including those with limited or challenged credit. The company purchases and services retail installment contracts and shares collections with dealers through structured programs. Founded in 1972 and headquartered in Southfield, Michigan, it trades on NASDAQ as CACC and operates across the U.S.