Staff Machine Learning Engineer
@ Revecore

Hybrid
$150,000
Hybrid
Full-time
Posted 22 hours ago

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Job Details

About Revecore

Start your next chapter at Revecore! For over 25 years, we have been at the forefront of specialized claims management, helping healthcare providers recover meaningful revenue and enhance quality patient care. Our culture is collaborative and diverse, offering a great work/life balance.

What We Offer

  • Comprehensive medical, dental, vision, and life insurance benefits
  • 12 paid holidays and flexible paid time off
  • 401(k) contributions
  • Employee Resource Groups
  • Career growth opportunities
  • Excellent work/life balance

Location

Remote – USA

Role Overview

As a Staff Machine Learning Engineer at Revecore, you will use your expertise in machine learning, exploratory data analysis, and software engineering to enhance productivity and efficiency in our underpayment business. Your work directly impacts revenue recovery for client hospitals by deploying scalable and robust machine learning models.

Key Responsibilities

  • Own end-to-end development, training, deployment, evaluation, and improvement of ML systems to rank claim opportunities.
  • Analyze and explore data to identify actionable opportunities from various data sources.
  • Research, implement, and launch new model architectures for business impact.
  • Collaborate with cross-functional teams including software engineers, data engineers, subject matter experts, product managers, and analysts.
  • Implement cloud MLOps best practices to streamline ML model life cycle.
  • Measure impact on key business metrics and refine models accordingly.
  • Share knowledge as you become the team expert and learn from peers.

Qualifications

  • Bachelor's degree in a data-centric field; advanced degrees are a plus.
  • Experience in developing and deploying machine learning models in production.
  • Strong Python skills, particularly with scikit-learn; familiarity with TensorFlow or PyTorch is a plus.
  • Ability to wrangle data, perform exploratory data analysis, and derive insights from visualizations.

Preferred Skills

  • Proficiency in statistical analysis and causal inference.
  • Experience with natural language processing (NLP) techniques and tools, like Hugging Face.
  • Experience with Spark and operating ML pipelines in a cloud environment (AWS, GCP, Azure).

Work at Home Requirements

  • A quiet, distraction-free environment.
  • A secure home internet connection with speeds >20 Mbps download and >10 Mbps upload.
  • A workspace accommodating all necessary equipment.

Equal Opportunity Employer

Revecore is an equal opportunity employer. We value diversity and encourage individuals of all backgrounds and abilities to apply.

Location Eligibility

Applicants must reside in the United States in one of the following states: Alabama, Arkansas, Delaware, Florida, Georgia, Iowa, Illinois, Indiana, Kansas, Kentucky, Louisiana, Massachusetts, Maine, Maryland, Michigan, Minnesota, Missouri, Mississippi, Montana, North Carolina, Nebraska, New Hampshire, Ohio, Oklahoma, Pennsylvania, Rhode Island, South Carolina, Tennessee, Texas, Virginia, Wisconsin, or West Virginia.

Key Skills/Competency

  • Machine Learning
  • Python
  • Data Analysis
  • MLOps
  • Cloud Deployment
  • Exploratory Data Analysis
  • NLP
  • Spark
  • Collaboration
  • Model Deployment

How to Get Hired at Revecore

🎯 Tips for Getting Hired

  • Customize your resume: Emphasize ML and cloud deployment experience.
  • Research Revecore: Understand their claims management and tech culture.
  • Highlight collaboration: Showcase team and cross-functional projects.
  • Prepare technical responses: Be ready for ML and data analysis questions.

📝 Interview Preparation Advice

Technical Preparation

Review Python and scikit-learn libraries.
Practice ML deployment on cloud platforms.
Study data wrangling and exploratory analysis.
Explore MLOps strategies for model lifecycle.

Behavioral Questions

Describe a challenging ML project experience.
Explain cross-team collaboration instances.
Discuss handling feedback on model performance.
Share instances of troubleshooting under pressure.

Frequently Asked Questions