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 team members and 500+ active projects for Fortune 500 clients such as HBO, Microsoft, Google and Starbucks.

The company's work runs from AI platforms to digital transformation, done alongside enterprise leaders.

The client is the leading provider of vehicle lifecycle solutions, headquartered in Chicago. Its platform serves the companies that build, insure and replace vehicles, using mobile, artificial intelligence and car technologies. It connects more than 350 insurance companies, over 24,000 repair facilities, hundreds of parts suppliers and dozens of third-party data and service providers, helping clients deliver better experiences to end consumers.

Responsibilities

• Design and build end-to-end document intelligence pipelines on AWS.
• Build and tune ML models that classify and segment documents and extract fields from them.
• Create data processing systems that handle PDFs of up to 2000 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, Pandas, ScikitLearn, TensorFlow or PyTorch, PyStats and Pydantic.
• Hands-on work with AWS ML engineering and deployment tools such as SageMaker, Lambda, CloudFormation/CDK and Step Functions.
• Advanced SQL and data modeling skills.
• Experience with GenAI for document intelligence: 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.
• Clear written and verbal communication, needed for a remote, largely asynchronous setup.
• Ability to explain complex technical problems concisely and propose iterative solutions.
• Experience taking a feature from concept to production, covering proposal, discussion and execution.
• Experience with document processing tools such as AWS Textract, Azure Document Intelligence or similar 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: algorithm selection, feature engineering, model training, hyperparameter tuning, supervised and unsupervised learning, building model pipelines, and using ML tools, libraries and frameworks.
• Experience with AWS big data services such as Redshift, S3, EMR and Glue.
• English at upper-intermediate level.

Conditions

Remote work with a largely asynchronous schedule.