Senior Machine Learning Engineer

Description

The company is an AI-first global tech firm with more than 25 years of engineering leadership, over 2,000 staff, and 500+ active projects for Fortune 500 clients such as HBO, Microsoft, Google, and Starbucks.

The company works with enterprise leaders on projects ranging from AI platforms to digital transformation.

The client is a leading provider of vehicle lifecycle solutions, headquartered in Chicago. It helps companies that build, insure, and replace vehicles develop the next generation of transportation, using mobile, artificial intelligence, and automotive technologies.

The client's platform links more than 350 insurers, over 24,000 repair facilities, hundreds of parts suppliers, and dozens of third-party data and service providers, helping its customers work faster and give end consumers a better experience.

Responsibilities

• Design and build end-to-end document intelligence pipelines on AWS.
• Develop and tune ML models that classify documents, split them into sections, and extract fields.
• Build data processing systems that can handle PDFs of up to 2,000 pages.
• Work with subject matter experts to define and refine extraction requirements.
• Take features from research through production deployment and monitoring.
• Set up evaluation frameworks and quality metrics for extraction accuracy.

Requirements

• Advanced Python skills, including native Python and libraries such as Pandas, scikit-learn, TensorFlow or PyTorch, PyStats, and Pydantic.
• Hands-on experience with AWS ML engineering and deployment tools such as SageMaker, Lambda, CloudFormation or CDK, and Step Functions.
• Advanced SQL and data modeling skills.
• Experience with GenAI for document intelligence, covering prompt engineering, RAG (Retrieval Augmented Generation), multi-modal vision and text models, and production deployment on AWS Bedrock or Azure OpenAI APIs.
• Experience designing experiments, including power analysis and hypothesis testing.
• Strong written and verbal communication, since the role is remote and largely asynchronous.
• Ability to explain complex technical problems clearly and concisely and to propose iterative solutions.
• Experience taking a feature from concept to production, including proposal, discussion, and execution.
• Experience with document processing tools such as AWS Textract, Azure Document Intelligence, or other OCR and layout detection systems.
• Experience with PDF and image processing libraries such as PyMuPDF, OpenCV, and Pillow.
• Experience in machine learning and data science, such as algorithm selection, feature engineering, model training, hyperparameter tuning, supervised and unsupervised learning, building model pipelines, and using ML frameworks.
• Experience with AWS big data services such as Redshift, S3, EMR, and Glue.
• Upper-intermediate English.

Conditions

The role is remote and mostly asynchronous.