Staff Machine Learning Engineer (Employer of Record)
Credit Acceptance3 months ago
Remote, IndiaStaff+
Responsibilities
- Explore and apply machine learning, deep learning, large language models, graph neural networks, reinforcement learning, recommendation systems, and causal inference to business problems.
- Define strategic ML/AI roadmaps with management and stakeholders and translate them into actionable delivery plans.
- Design, build, deploy, monitor, and continuously optimize scalable, secure, production-grade ML and generative AI systems.
- Architect enterprise LLM-powered applications, multi-agent systems, RAG pipelines, intelligent routing, orchestration, and evaluation frameworks.
- Implement parameter-efficient fine-tuning, model quantization, and domain adaptation for foundation models.
- Build ML and GenAI platforms, inference pipelines, batch and streaming services, and ML/LLM operations capabilities.
- Ensure security, scalability, reliability, architectural integrity, and compliance of AI solutions.
- Troubleshoot complex technical issues and improve operational efficiency and system reliability.
- Mentor MLEs and other data professionals on design principles, coding standards, engineering practices, and AI productivity tools.
Requirements
- PhD in Computer Science, Statistics, Economics, or a relevant technical field with at least 5 years of relevant experience, or an MS with at least 8 years of experience in machine learning and software engineering.
- At least 6 years of hands-on experience designing, building, and deploying AI, machine learning, deep learning, and generative AI models.
- At least 4 years of experience building and deploying AI/ML applications, including generative AI, recommendation systems, and reinforcement learning.
- Strong understanding of mathematics, statistics, computer science, and engineering principles for AI infrastructure and applications.
- Experience with reinforcement learning, recommendation systems, transformers, fine-tuned LLMs, causal inference, regression, and production-grade ML services.
- Experience with ML/LLM operations, including model versioning, automation, observability, automated training, and monitoring.
- Experience with PyTorch, TensorFlow, and Hugging Face Transformers.
- Preferred experience in automotive ML/AI systems, model interpretability, responsible AI, advanced experimentation, and visualization.
- Experience designing DAG-based pipelines with tools such as Kubeflow, DVC, or Ray.
- Ability to build batch and streaming microservices exposed through gRPC or GraphQL endpoints.
- Experience with Databricks MLflow, Databricks Model Serving, model versioning, and production ML deployments.
- Experience with parameter-efficient fine-tuning, model quantization, quantization-aware fine-tuning, multimodal AI, and prompting strategies such as Chain-of-Thoughts, Tree-of-Thoughts, and Graph-of-Thoughts.
- Experience building conversational, content-generation, code-generation, and multimodal LLM systems.
- Proficiency with data and AI technologies such as Apache Airflow, Spark, Flink, Kafka or Kinesis, Snowflake, and Databricks.
- Deep understanding of at least three of data mining, advanced statistics, machine learning, deep learning, or natural language processing.
- Strong problem-solving skills and experience collaborating with engineering, product, business operations, legal, and data teams.
Benefits
- The role is hired through an Employer of Record partner in India, with local payroll, benefits, statutory coverage, and legally compliant employment.
- The employee will work full-time and be fully aligned with Credit Acceptance's global team and day-to-day responsibilities.
- The role requires regular overlap with U.S. business hours to support collaboration with global team members.
- Employees must comply with company policies, processes, and guidelines, with attendance as required by the department.
Tech Stack
Apache AirflowApache FlinkApache KafkaApache SparkDatabricksDVCGraphQLgRPCHugging Face TransformersMLflowPyTorchSnowflakeTensorFlowXGBoost
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