Machine Learning Engineer

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Full-time / Seattle Area-based / Remote

Company information

Our healthcare systems are facing an existential threat. In the wake of a global pandemic, providers are struggling to serve their patients due to constrained resources and outsized spending. Today's healthcare systems often span multiple regions and practices, generating conflicting and confusing operating data. To stay in business, healthcare operators need to make timely, efficient, and effective decisions around labor, supplies, and operating revenues, and they need data clarity to do so.

Xolved delivers clear and actionable data that healthcare organizations can trust. Our unified platform untangles enterprise complexity and empowers healthcare leaders to respond to their biggest financial challenges. We drive outcomes through greater intelligence and precision, allowing healthcare organizations to make powerful decisions and, ultimately, execute on what matters most: making people healthier.

We are seeking team members who bring an innovative and agile approach to their work, love tackling complex challenges head-on, and are ready to make a real impact.

Job description

Xolved delivers an AI model trained on healthcare expertise and healthcare data structures which enables automation of business logic and data operations. We are looking for an expert in machine learning to help us to train AI models and to leverage Large Language Models (LLMs) as we build our Intelligent Assistant for healthcare operators. This work includes natural language understanding, intent classification and natural language to code generation.

The ideal candidate will be passionate about artificial intelligence, especially Generative AI, and will be up-to-date with the latest developments in the field.

Xolved is headquartered in Bellevue, and this role could be fully remote or hybrid.


  • Understanding business objectives and developing AI models that help to achieve them, along with metrics to track their progress
  • Analyzing the ML algorithms to solve a given problem and rank them by their success probability
  • Exploring and visualizing data to gain model understanding
  • Supervising the data acquisition process
  • Finding available datasets from public/private data sources that could be used for training
  • Training models, tuning their hyperparameters, and improving model performance
  • Working with large language models and enhancing their capabilities to solve our own use case through fine-tuning and prompt engineering
  • Analyzing the errors of the model and designing strategies to overcome them


  • 3+ years working on machine learning projects, including training, fine tuning and refining models
  • Experience with Python scientific stack, PyTorch, and creating Jupyter/Colab notebooks
  • Ability to communicate machine learning concepts and results effectively through writing and visualization
  • Experience with training and/or deploying ML models with Amazon AWS (preferrably Sagemaker) or Microsoft Azure
  • Experience with Linux and command line tools
  • Bachelor’s degree in Computer Science or related fields

Preferred qualifications

  • Experience with building interactive web demos that serve generative ML models
  • Experience with the open-source ML ecosystem (HuggingFace, Weight & Biases, etc.)
  • Has startup mindset and has flexibility and a level of comfort with early-stage iterating and moving fast

Don’t meet every single requirement listed above? That's ok. If you’re excited about this role and our mission, but your experience doesn’t align perfectly with every qualification listed here, we encourage you to apply anyways. You may be the right candidate for this or other roles within the broader MVL portfolio.

We are an equal opportunity employer and highly value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

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Interested applicants should include a resume and cover letter.