Staff Machine Learning Engineer – Platform, MLOPS

🕒 il y a 28 jours

🚗 Michigan – Distant

🚗 Michigan – Remote

💵 $154 120 - $226 042 / an

⏰ Temps Plein

🔴 Expert

🏗️ Ingénieur Plateforme

🦅 Parrain de Visa H1B

Cette entreprise a parrainé des visas H1B par le passé.

👻 Score fantôme 0%

Ce que ça veut dire : la probabilité que personne ne recrute vraiment pour cette offre.Comment le lire : 60 % ou plus, on pense que c'est une offre fantôme. Un score plus bas est moins inquiétant.Comment on la calcule : ancienneté, republications, postes ouverts vs taille de l'équipe, rapidité de clôture, et si le salaire est indiqué.À noter : une estimation à partir de données publiques, pas un jugement sur l'entreprise. De vraies offres peuvent avoir un score élevé, alors prenez-le comme un indice, pas comme un fait.

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Credit Acceptance

Site WebLinkedInTous les Emplois

1001 - 5000 employés

Fondée en 1972

💸 Finance

💳 Fintech

🚘 Automobile

Finance • Fintech • Automotive

Credit Acceptance est une entreprise spécialisée dans la fourniture de solutions de financement automobile aux particuliers, y compris ceux ayant un mauvais historique de crédit ou aucun historique de crédit. Avec plus de 50 ans d'expérience, Credit Acceptance a aidé plus de 4 millions de consommateurs à obtenir un financement pour l'achat de véhicules. L'entreprise opère un réseau de plus de 15 000 concessionnaires et offre aux clients la possibilité de se pré-qualifier pour un financement automobile, ce qui n'affecte pas leur cote de crédit. De plus, Credit Acceptance fournit des ressources éducatives et des outils, tels que des calculateurs de paiement et de prix d'automobile, pour aider les clients à gérer leurs finances et comprendre leurs options de crédit. La mission de l'entreprise est de donner une chance en offrant des solutions financières qui permettent aux individus d'améliorer leur situation financière et d'assurer un moyen de transport fiable.

Description

• Own and operate the platform that every ML and AI model at Credit Acceptance runs on • 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 runtime health for production models through monitoring, alerting, drift and quality-regression detection, latency and throughput objectives, capacity, autoscaling, and incident response through root cause and corrective action • Operate the agent runtime layer through the enterprise AI Gateway and MCP Gateway; migrate agents onto the governed path and maintain scoped, versioned, least-privilege tool surfaces • Own production evaluation of agents, including online scoring, behavioral monitoring, drift detection, sampling and judge pipelines, and release gates • Build and maintain observability and evaluation infrastructure, including multi-turn and multi-step agent trace and telemetry capture, logging standards, pipeline plumbing, and data contracts • Measure and manage platform unit economics, including cost per inference, document, and interaction, and provide cost analysis for build-versus-buy and hosting decisions • Deliver reusable pipeline templates, deployment patterns, reference implementations, and internal tooling to enable adoption of the standard ML platform path • Partner with Cloud Engineering, Data Engineering, Security, and SRE on enterprise governance, identity, and observability • Respond to AI-specific production incidents such as prompt injection, rogue-agent cost spikes, data-classification exposure, delegation abuse, and model endpoint failures; drive corrective actions to closure • Maintain architecture documentation and system diagrams for the ML platform • Mentor engineers and interns on production ML practices and raise operating standards through design and code review • Work from home with occasional planned travel to the assigned Southfield, Michigan office; may work at that office if requested by the team member • Remain compliant with company policies, processes, and legal guidelines • Perform other duties as assigned; attendance as required by department

🎯 Exigences

• 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 • 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 • Strong Python and SQL, with production-quality engineering practice including 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 • Working knowledge of operational differences between LLM/GenAI workloads and traditional ML, including token cost and latency behavior, 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 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 • Ability to communicate technical and non-technical trade-offs clearly in writing • Preferred: production experience with GPU-backed model serving, OpenTelemetry and enterprise observability platforms such as Dynatrace, 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 such as Agent2Agent (A2A) • Ability to communicate complex technical information verbally and in writing to all levels, including senior leadership • Ability to solve problems at the source with simple, working solutions • Prompt and effective incident, task, and project resolution • Demonstrated ability and motivation to teach others • Ability to build relationships across and vertically throughout the organization • Ability to prioritize and execute tasks in a high-pressure environment • Required degrees must have been earned at accredited institutions of higher education recognized by the Council for Higher Education Accreditation or equivalent

🏖️ Avantages

• Annual variable bonus of cash and equity, between 10-20% • 401(K) match • Adoption assistance • Parental leave • Tuition reimbursement • Comprehensive medical, dental, and vision coverage • Many nonstandard benefits • Potential premium on top of the posted range for candidates residing in San Francisco, Seattle, Boston, New York City, Los Angeles, and San Diego zones

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