Senior Machine Learning Engineer

As a Senior Machine Learning Engineer (individual contributor) at Revecore, you will:

Use your expertise in machine learning, exploratory data analysis, and software engineering to enhance the productivity and efficiency of our underpayment business. You will work on projects with purpose, such as prioritizing claims based on expected recovery dollars and improving our claim-remit matching process.

This is a modeling team that owns the model deployment process. You won’t be creating dashboards or pivot tables. You won’t just build POCs. Our team increases revenue and decreases costs: you will deploy your work and see the results as we increase our client hospitals’ revenue.

The Role:

Own end-to-end development, training, deployment, evaluation, and improvement of machine learning systems to rank claim opportunities.

Analyze and explore data to identify actionable opportunities from internal and 3rd party data.

Research, implement, and launch new model architectures that drive business impact.

Partner and collaborate with cross-functional teams of software engineers, data engineers, subject matter experts, product managers, and analysts to design and build practical solutions.

Implement cloud MLOps and AIOps best practices to streamline the development, deployment, and maintenance of machine learning models.

Continuously measure the impact of the AI-enabled workflows on key business metrics and use these measurements to improve the machine learning models and workflows.

Learn from and teach your teammates. You will be the team’s expert in your specialization, and you will learn from experts in theirs.

Own a workstream. You’ll be the technical lead for the workstream, partnering with others to deliver. You’ll also work on other projects, but this workstream will be one of your key successes.

You’ll be successful if you have:

An urge to question assumptions, and to get it right (or at least good enough) even if your first idea is wrong.

A commitment to collaborate, rather than go off in a corner only to appear when you need to submit a pull request.

A bachelor's degree in any data-centric field. Scientific thinking is a must.

Experience working in a similar role, with a focus on machine learning or data science.

Experience developing and deploying machine learning models in a production environment.

Strong experience with Python, including scikit-learn. TensorFlow or PyTorch is a plus.

Ability to wrangle data, perform exploratory data analysis, and draw insights from visualizations.

It would also be great if you have:

Intuition about data developed by doing statistics and/or research.

Applied experience with contemporary natural language processing (NLP) techniques and tools (e.g., entity extraction, transformers, Hugging Face).

Experience with Spark.

Experience with operating ML pipelines in a cloud platform (e.g., AWS, GCP, Azure).

A master's degree, Ph.D., or other experience demonstrating scientific thinking.

Work at Home Requirements:

• A quiet, distraction-free environment to work from in your home.
• A secure internet connection is required.
• Home internet with speeds >20 Mbps for downloads and >10 Mbps for uploads.
• The workspace area accommodates all workstation equipment and related materials and provides adequate surface area to be productive.

Employment is contingent upon eligibility to work in the United States, verification of employment history, and successful completion of a background check.