Middle Machine Learning Engineer

externaljobboards; United States; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Remote-friendly

International projects

Healthcare

Learning reimbursement

Job summary

externaljobboards is seeking a Middle Machine Learning Engineer to build document intelligence pipelines that extract structured data from large PDFs. The role sits at the intersection of applied ML, GenAI, and scalable AWS engineering, supporting a vehicle lifecycle platform for insurers, repair facilities, and parts suppliers.

You will design end-to-end pipelines on AWS, improve ML models for classification and extraction, handle PDFs up to 2000 pages, and work with subject matter experts to

Qualifications

• Strong Python experience with ML and data tools.
• Hands-on AWS ML and deployment tooling experience.
• Advanced SQL and data modeling skills.
• GenAI experience for document intelligence and production deployment.
• Experiment design, power analysis and hypothesis testing.
• Strong written and verbal communication for async remote work.

Responsibilities

• Design end-to-end document intelligence pipelines on AWS.
• Develop and optimize ML models for document classification and extraction.
• Build scalable data processing for PDFs up to ~2,000 pages.
• Collaborate with SMEs to refine extraction requirements.
• Take features from research to production and monitor performance.
• Establish evaluation frameworks and quality metrics for extraction accuracy.

Skills

Python

Pandas

Scikit-learn

TensorFlow

PyTorch

PyStats

Pydantic

SQL

Experiment design

GenAI concepts

English communication

Tools

AWS ML

SageMaker

Lambda

CloudFormation / CDK

Step Functions

AWS Bedrock / Azure OpenAI

Textract / Document AI tools

PyMuPDF / OpenCV / Pillow

Redshift / S3 / EMR / Glue

Job description

Role overview

A machine learning team is seeking a Middle Machine Learning Engineer to build document intelligence pipelines that extract structured data from large, multi-page PDFs. The role sits at the intersection of applied ML, GenAI, and scalable AWS engineering, supporting a vehicle lifecycle platform serving insurers, repair facilities, and parts suppliers.

Responsibilities

• Design and implement end-to-end document intelligence pipelines on AWS
• Develop and optimize ML models for document classification and field extraction
• Build scalable data processing systems capable of handling PDFs up to roughly 2,000 pages
• Collaborate with subject matter experts to define and refine extraction requirements
• Take features from research through production deployment and ongoing monitoring
• Establish evaluation frameworks and quality metrics for extraction accuracy

Requirements

• Strong Python experience with Pandas, Scikit-learn, TensorFlow or PyTorch, PyStats, and Pydantic
• Hands‑on experience with AWS ML and deployment tooling such as SageMaker, Lambda, CloudFormation or CDK, and Step Functions
• Advanced SQL and data modeling skills
• Practical GenAI experience for document intelligence, including prompt engineering, retrieval-augmented generation, multi‑modal models, and production deployment via AWS Bedrock or Azure OpenAI
• Background in experiment design, including power analysis and hypothesis testing
• Upper-Intermediate English, with strong written and verbal communication suited to remote, asynchronous work

Nice to have

• Experience with document processing tools such as AWS Textract or Azure Document Intelligence
• Familiarity with PDF and image libraries such as PyMuPDF, OpenCV, or Pillow
• Broader machine learning and data science experience
• Exposure to AWS big data services like Redshift, S3, EMR, and Glue

Benefits and work setup

• Remote‑friendly flexibility
• International project opportunities
• Medical healthcare, well‑being initiatives, and recognition programs
• Learning and certification reimbursement, plus sports compensation and referral bonuses