Staff Machine Learning Engineer, Platform (MLOps)

Requirements:

• Bachelors degree in Computer Science, Engineering, Statistics, or a related technical field and at least 7 years of relevant experience; alternatively, a masters degree in one of these fields and at least 5 years of relevant experience.
• At least 5 years of experience building and operating production ML or AI systems, with direct ownership of at least two of the following: training or inference pipelines, model-serving infrastructure, model registry and versioning, or production monitoring and alerting.
• Demonstrated ownership of a production ML or AI service throughout its operating life, including deployment, monitoring, incident diagnosis, and delivery of fixes.
• Strong Python and SQL skills, with production-quality engineering practices such as version control, testing, code review, and CI/CD applied to ML workloads.
• Hands-on experience using a cloud ML platform in production. AWS and Databricks experience is strongly preferred, including model serving, job orchestration, and a model registry or experiment-tracking system such as MLflow.
• Working knowledge of operational differences between LLM and GenAI workloads and traditional ML, including token cost, latency, caching, batching, and non-deterministic output.
• Experience running LLM or agent applications behind a gateway or proxy, and developing practices for model routing and fallback, credential and key management, rate limiting, and budget enforcement.
• Working knowledge of agent tool-calling architecture, including the Model Context Protocol (MCP), tool scoping and authorization, and gateway-brokered tool access.
• Experience with containerization and infrastructure as code.
• Ability to clearly communicate technical and non-technical trade-offs in writing to engineering and non-engineering audiences.
• Must reside in or be willing to relocate to the greater Southfield, Michigan area; greater Bellevue, Washington area; or San Francisco Bay Area, California.
• Preferred: Production experience with GPU-backed model serving, including autoscaling under concurrent load, cold-start management, and cost control.
• Preferred: Experience with OpenTelemetry and an enterprise observability platform such as Dynatrace, including instrumentation of AI workloads.
• Preferred: Experience with model-serving efficiency techniques such as quantization, parameter-efficient fine-tuning, distillation, or inference optimization.
• Preferred: Experience with data governance and access control on a lakehouse platform such as Databricks Unity Catalog.
• Preferred: Experience operating AI systems in a regulated industry, particularly financial services, including auditability, retention, and access-control requirements.
• Preferred: Experience operating agentic or multi-step AI systems in production, including tracing and debugging multi-turn behavior.
• Preferred: Hands-on experience with a managed agent or tool gateway, such as AWS Bedrock AgentCore Gateway, and running MCP servers in a governed environment.
• Preferred: Familiarity with agent interoperability and identity standards, including Agent2Agent (A2A)-style delegation, agent discovery and capability advertisement, and workload identity for non-human actors.
• Ability to communicate complex technical information verbally and in writing to all levels, including senior leadership; solve problems at their source with practical solutions; respond promptly to incidents and projects; teach others; build trust and strong working relationships; and prioritize and execute effectively in high-pressure environments.
• Our role competencies include customer empathy, engineering excellence, collaboration across teams, and an owners mindset. We value resilience, respect, insight, direct communication, and accountability.

Responsibilities:

• Own end-to-end deployment of ML and GenAI models, including training and inference pipelines, model registry and versioning, serving endpoints, and controlled promotion through development, QA, and production.
• Maintain the runtime health of production models through monitoring, alerting, drift and quality-regression detection, latency and throughput objectives, capacity and autoscaling management, and incident response through root-cause analysis and corrective action.
• Operate the agent runtime layer by routing production agents through the enterprise AI Gateway and MCP Gateway, migrating existing agents to this governed path, and keeping tool access scoped, versioned, and least-privilege.
• Own ongoing production evaluation of agents, including online scoring and behavioral monitoring, comparisons with pinned baselines, sampling and judge pipelines, and release gates that prevent regressions from shipping.
• Build and maintain the observability and evaluation foundations used by other teams, including trace and telemetry capture for multi-turn and multi-step agents, logging standards, evaluation pipeline plumbing, and supporting data contracts.
• Measure and manage platform unit economics, including cost per inference, document, and interaction, and provide cost analysis to inform build-versus-buy and hosting decisions.
• Create reusable pipeline templates, deployment patterns, reference implementations, and internal tools that make the standard path faster and easier for product teams to adopt.
• Partner with Cloud Engineering, Data Engineering, Security, and SRE to integrate the ML platform with enterprise governance, identity, and observability.
• Respond to AI-specific production incidents—including prompt injection, unexpected agent cost spikes, data-classification exposure through tool calls, delegation abuse, and model endpoint failures—and drive issues through corrective action.
• Maintain accurate ML platform architecture documentation and system diagrams for use in design reviews.
• Mentor engineers and interns in production ML practices and strengthen engineering standards through design and code reviews.

Technologies:

• AI
• AWS
• CI/CD
• Cloud
• Databricks
• Dynatrace
• Fine-tuning
• Support
• LLM
• MCP
• MLflow
• Model Serving
• OpenTelemetry
• Python
• SQL
• Security
• Unity
• GameDev

More

At Credit Acceptance, we are building on a successful business while investing in technology, data, AI, and continuous improvement. Our Engineering teams collaborate across the organization to address complex challenges, build impactful solutions, and support informed business decisions. We foster curiosity, learning, innovation, and opportunities for Team Members to shape change and grow their careers. This position offers a base salary range of $184,943–$258,920 and eligibility for an annual cash and equity incentive of 10–20% of base salary; final compensation depends on factors including hub location, relevant skills and experience, education, and certifications. We use a hybrid work model with two work-from-home days and three days in the local office each week. Our benefits include a 401(k) match, adoption assistance, parental leave, tuition reimbursement, comprehensive medical, dental, and vision coverage, and additional benefits. We value positivity, respect, insight, direct communication, and accountability.

last updated 43 week of 2026