Staff Machine Learning Engineer, Platform (MLOPS)

Credit Acceptance Corporation•San Francisco, CA

8d•Hybrid

About The Position

Credit Acceptance is seeking a Staff Machine Learning Engineer to build and operate its ML and GenAI platform. The role owns the end-to-end deployment path for models and agents, including training and inference pipelines, model registry and versioning, serving endpoints, and controlled promotion across development, QA, and production. It also owns production runtime health, agent evaluation, observability and evaluation infrastructure, and platform unit economics. The engineer will operate a governed agent runtime, routing agents through enterprise AI and MCP gateways, and will build reusable templates, deployment patterns, reference implementations, and tooling that make the standard platform path easy for product teams to adopt. The role partners with Cloud Engineering, Data Engineering, Security, and SRE; responds to AI-specific production incidents; maintains accurate platform architecture documentation; and mentors engineers and interns. The position follows a hybrid work model: two work-from-home days and three standard in-office days each week. Candidates must reside in or be willing to relocate to the greater Southfield, Michigan area; greater Bellevue, Washington area; or San Francisco Bay Area, California. The position offers a base salary of $184,943–$258,920 and is eligible for an annual variable incentive of cash and equity between 10% and 20% of base salary. Final compensation depends on factors including hub location, skills, experience, industry background, education, and certifications.

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 direct ownership of at least two of: 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 through its full operating life, including deployment, monitoring, incident response, failure diagnosis, and delivery of a fix.
Strong Python and SQL skills, with production-quality engineering practices including version control, testing, code review, and CI/CD applied to ML workloads.
Hands-on experience with a cloud ML platform in production.
Working knowledge of operational differences between LLM and GenAI workloads and traditional ML, including token cost and latency, caching and batching, and non-deterministic output.
Experience 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.
Working knowledge of agent tool-calling architecture, including MCP servers, tool scoping and authorization, and gateway-brokered tool access.
Experience with containerization and infrastructure as code.
Ability to communicate technical and non-technical trade-offs clearly in writing to engineers and non-engineers.
Must reside in or be willing to relocate to the greater Southfield, Michigan area; greater Bellevue, Washington area; or San Francisco Bay Area, California.
Ability to communicate complex technical information verbally and in writing to all levels, including senior leadership.
Ability to solve problems at the source by offering simple, working solutions.
Respond promptly and effectively to incidents, tasks, and projects.
Demonstrated ability and motivation to teach others.
Ability to build trust and solid relationships across and vertically throughout the organization.
Ability to prioritize and execute tasks effectively in a high-pressure environment.
Required degrees must be from institutions of higher education accredited by the Council for Higher Education Accreditation or equivalent.

Nice To Haves

AWS and Databricks experience, including model serving, job orchestration, and a model registry or experiment tracking system such as MLflow.
Production experience with GPU-backed model serving, including autoscaling under concurrent load, cold-start management, and cost control.
Experience with OpenTelemetry and an enterprise observability platform such as Dynatrace, including instrumentation of AI workloads.
Experience with model-serving efficiency techniques such as quantization, parameter-efficient fine-tuning, distillation, or inference optimization.
Experience with data governance and access control on a lakehouse platform such as Databricks Unity Catalog.
Experience operating AI systems in a regulated industry, particularly financial services, including auditability, retention, and access-control requirements.
Experience with agentic or multi-step AI systems in production, including tracing and debugging multi-turn behavior.
Hands-on experience with a managed agent or tool gateway, such as AWS Bedrock AgentCore Gateway, and running MCP servers in a governed environment.
Familiarity with agent interoperability and identity standards, including Agent2Agent-style delegation, agent discovery and capability advertisement, and workload identity for non-human actors.

Responsibilities

Own the end-to-end deployment path for ML and GenAI models, including training and inference pipelines, model registry and versioning, serving endpoints, and controlled promotion across development, QA, and production.
Own production model runtime health, including monitoring, alerting, drift and quality-regression detection, latency and throughput objectives, capacity and autoscaling, and incident response through root cause and corrective action.
Operate the agent runtime layer by routing production agents through enterprise AI and MCP gateways, migrating existing agents to that governed path, and keeping tool surfaces scoped, versioned, and least-privilege.
Own production agent evaluation, including online scoring and behavioral monitoring, regression and drift detection against pinned baselines, sampling and judge pipelines, and release gates.
Build and maintain observability and evaluation infrastructure, including trace and telemetry capture, multi-turn and multi-step agent traces, logging standards, evaluation pipeline plumbing, and supporting data contracts.
Measure and manage platform unit economics, including cost per inference, document, and interaction, and produce cost analysis to inform build-versus-buy and hosting decisions.
Deliver reusable pipeline templates, deployment patterns, reference implementations, and internal tooling that help product teams adopt the standard platform path.
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 attempts, rogue-agent cost spikes, data-classification exposure through tool calls, delegation abuse, and model endpoint failures, and drive corrective actions to completion.
Maintain accurate ML platform architecture documentation and system diagrams for design reviews.
Mentor engineers and interns on production ML practices and raise operating standards through design and code review.