Staff Machine Learning Engineer – Platform, MLOPS

This is a fully remote position, open to applicants in Michigan.

📋 Description

• Take ownership of and manage the platform that underpins all ML and AI models at Credit Acceptance.

• Oversee the comprehensive deployment process for ML and GenAI models, encompassing training and inference pipelines, model registry and versioning, serving endpoints, and controlled promotion across development, QA, and production environments.

• Ensure runtime health for production models through consistent monitoring, alerting, drift and quality-regression detection, as well as managing latency and throughput objectives, capacity, autoscaling, and incident response via root cause analysis and corrective measures.

• Manage the agent runtime layer through the enterprise AI Gateway and MCP Gateway; transition agents onto the governed path while maintaining scoped, versioned, least-privilege tool surfaces.

• Supervise the production evaluation of agents, including online scoring, behavioral monitoring, drift detection, sampling and judge pipelines, as well as release gates.

• Develop and sustain observability and evaluation infrastructure, which includes multi-turn and multi-step agent tracing and telemetry capture, logging standards, pipeline configurations, and data contracts.

• Evaluate and manage platform unit economics, focusing on cost per inference, documentation, and interaction, while providing cost analysis for build-versus-buy and hosting decisions.

• Create reusable pipeline templates, deployment patterns, reference implementations, and internal tools to facilitate the adoption of the standard ML platform pathway.

• Collaborate with Cloud Engineering, Data Engineering, Security, and SRE on enterprise governance, identity, and observability strategies.

• Address AI-specific production incidents like prompt injection, rogue-agent cost spikes, data-classification exposure, delegation abuse, and model endpoint failures; ensure corrective actions are effectively implemented.

• Maintain architecture documentation and system diagrams related to the ML platform.

• Mentor engineers and interns on production ML practices and enhance operating standards through design and code reviews.

• Work remotely with occasional planned travel to the assigned office in Southfield, Michigan; may work at that office if requested by team members.

• Comply with company policies, processes, and legal regulations.

• Perform additional duties as assigned; attendance as needed by the department.

⛳️ Requirements

• Bachelor’s degree in Computer Science, Engineering, Statistics, or a relevant technical field with a minimum of 7 years of related experience, or a Master’s degree in one of those fields with at least 5 years of relevant experience.

• Over 5 years of experience in building and operating production ML or AI systems, with direct responsibility for at least two of the following: training or inference pipelines, model serving infrastructure, model registry and versioning, or production monitoring and alerting.

• Proven ownership of a production ML or AI service throughout its entire operational lifecycle.

• Proficient in Python and SQL, employing production-quality engineering practices such as version control, testing, code reviews, and CI/CD specifically applied to ML workloads.

• Hands-on experience with a cloud ML platform in production; AWS and Databricks are strongly preferred, encompassing model serving, job orchestration, and a model registry or experiment tracking system like MLflow.

• Understanding of the operational differences between LLM/GenAI workloads and traditional ML, including token costs, latency behavior, caching and batching, as well as non-deterministic outputs.

• Experience in running LLM or agent applications behind a gateway or proxy layer, including model routing and fallback, credential and key management, rate limiting, and budget enforcement.

• Familiarity with tool-calling architecture for agents, including the Model Context Protocol, MCP servers, tool scoping and authorization, and gateway-brokered tool access.

• Experience with containerization and infrastructure as code.

• Strong ability to communicate technical and non-technical trade-offs effectively in writing.

• Preferred: production experience with GPU-backed model serving, OpenTelemetry, and enterprise observability platforms such as Dynatrace; familiarity with model serving efficiency techniques, Databricks Unity Catalog, regulated-industry AI systems, agentic or multi-step AI systems, managed agent/tool gateways such as AWS Bedrock AgentCore Gateway, governed MCP servers, and emerging agent interoperability and identity standards like Agent2Agent (A2A).

• Ability to articulate complex technical information both verbally and in writing to various audiences, including senior leadership.

• Capacity to resolve problems at the source with effective, straightforward solutions.

• Prompt and efficient resolution of incidents, tasks, and projects.

• Demonstrated ability and enthusiasm for teaching others.

• Proficient in establishing relationships across different levels within the organization.

• Ability to prioritize and execute tasks in a high-pressure environment.

• Required degrees must be obtained from accredited institutions of higher education recognized by the Council for Higher Education Accreditation or equivalent.

🏝️ Benefits

• Annual variable bonus of cash and equity, ranging from 10-20%.

• 401(K) matching.

• Adoption assistance.

• Parental leave.

• Tuition reimbursement.

• Comprehensive medical, dental, and vision coverage.

• A variety of nonstandard benefits.

• Potential premium on top of the posted range for candidates located in San Francisco, Seattle, Boston, New York City, Los Angeles, and San Diego zones.