Staff Machine Learning Engineer (AI Team)

About Credit Acceptance

Credit Acceptance is an award-winning, stable financial company and one of the largest used car finance companies in the United States. Our world-class culture is shaped by dedicated team members who share a drive to succeed.

Remote / India (Employer of Record) This position is hired through our Employer of Record (EoR) partner in India. While your legal employer will be our EoR partner, you will be fully integrated into the global Credit Acceptance team for day-to-day work, collaboration, and performance expectations.

• Compensation (CTC) Range: ₹ 63,55,839 – ₹ 93,21,897 (Final CTC depends on skills, experience, and background)
• Shift Expectation: Regular overlap with U.S. business hours to support global collaboration.
• Apply

Role Overview

As a Staff Machine Learning Engineer (MLE) within the AI Team, you will lead the development of AI-powered solutions across multiple business areas. You will partner with business and engineering stakeholders to shape the vision for achieving the company’s strategic goals and co-lead the roadmap to deliver innovative solutions for dealers, consumers, and team members.

This role requires a deep technical background in decision science, machine learning, and generative AI, alongside a proven track record of implementing large-scale automated solutions.

Key Responsibilities

Machine Learning Outcomes

• Advanced Modeling: Explore and apply advanced ML techniques (LLMs, Deep Learning, Graph Neural Networks) to solve complex organizational challenges.
• Strategic Planning: Collaborate with management to translate high-level roadmaps into actionable quarterly plans.
• System Design & Reliability: Design scalable, secure, and well-tested AI/ML systems; troubleshoot and optimize system reliability and operational efficiency.
• Team Leadership & Mentorship: Guide, mentor, and upskill a globally distributed team of MLEs in design principles, coding standards, and AI productivity tools.
• Core Domain Implementations:
  • Recommendations: Personalize guidance across surfaces using deep learning and Bayesian contextual multi-armed bandits.
  • Growth & Lifecycle: Foster long-term growth using data-driven causality, incrementality, and ML models (e.g., XGBoost, Causal Meta-Learner-based models).

Generative AI Outcomes

• Enterprise LLM Lifecycle: Architect and implement enterprise-grade LLM-powered solutions from requirements gathering to production deployment, monitoring, and optimization.
• Multi-Agent Systems: Develop multi-agent GenAI systems using frameworks like LangChain and LlamaIndex to orchestrate workflows across retrieval, data operations, and compliance.
• Advanced RAG Pipelines: Engineer robust Retrieval Augmented Generation (RAG) pipelines incorporating hybrid retrieval, reranking, query expansion, and contextual compression.
• Fine-Tuning & Quantization: Implement parameter-efficient fine-tuning (LoRA, QLoRA, PEFT) to adapt foundation models while optimizing for inference cost and latency.
• Routing & Evaluation: Develop intelligent routing systems to manage conversation states across specialized agents, and build evaluation frameworks to measure factuality, coherence, and alignment.

Position Requirements

Required Education & Experience

• Educational Background:
  • PhD in Computer Science, Statistics, Economics, or a relevant technical field with 5+ years of relevant experience.
  • OR an MS degree with 8+ years of experience in machine learning and software engineering.
• Core ML Skills (6+ Years): Hands-on experience designing, building, and deploying AI (ML, DL, Gen-AI) models—including Reinforcement Learning, Recommendation Systems, Transformers, Causal Inference, and Regressions.
• GenAI Skills (4+ Years): Direct experience building and deploying GenAI applications and LLMs, backed by a solid understanding of foundation concepts and engineering infrastructure.
• Problem-Solving: Strong analytical skills with a distinct bias for action.

Technical Knowledge & Technical Stack Requirements

• Frameworks: Proficiency with PyTorch, TensorFlow, and Hugging Face Transformers.
• Core Expertise: Deep understanding of at least three areas: data mining, advanced statistics, machine learning, deep learning (including NLP).
• MLOps: Hands-on expertise in scaling production-grade ML/LLM services (versioning, automation, observability, and automated training).
• Application Experience: Proven experience building conversational AI (Text, Voice), content generation, or code generation systems.

Preferred Qualifications

• Industry Experience: Experience in the automotive or auto-finance industry, especially building ML/AI systems within local and central regulatory frameworks.
• Advanced GenAI Tech: Quantization-aware fine-tuning and advanced prompting strategies (Chain-of-Thought, Tree-of-Thought, Graph-of-Thought).
• Data & Orchestration Pipelines: Experience designing pipelines using DAGs (Kubeflow, DVC, Ray, Apache Airflow) and big data tools (Spark, Flink, Kafka/Kinesis).
• Microservices: Ability to construct batch and streaming microservices exposed as gRPC and/or GraphQL endpoints.
• Databricks Ecosystem: Mastery of Databricks MLflow for lifecycle management and Databricks Model Serving for production deployments.
• Cloud Data Warehousing: Familiarity with Snowflake or Databricks ecosystems.
• Responsible AI: Expertise in model interpretability, advanced experimentation, and responsible AI practices.

Core Competencies & Company Values

Core Competencies

• Customer Empathy: Actively putting oneself in the customer’s shoes to build a better, customer-centric experience.
• Engineering Excellence: Bringing great craftsmanship, high standards, and innovation to deliver outstanding products.
• One Team Mindset: Collaborative approach across boundaries with shared goals and open communication.
• Owner’s Mindset: Taking deep accountability, responsibility, and strategic ownership of your domain.

Our Values

• Positive: Maintaining resiliency and focusing on solutions.
• Respectful: Collaborating and actively listening.
• Insightful: Cultivating innovation and making quality, data-driven decisions.
• Direct: Effectively communicating and conveying courage.
• Earnest: Taking accountability, applying feedback, and setting clear priorities.