Applied Machine Learning Engineer

How You’ll Make an Impact

As an Applied Machine Learning Engineer, you will collaborate with architects, data scientists, agentic AI developers, platform engineers, and the product team to build advanced AI and ML capabilities into our platform. Your work will drive innovation in generative AI and beyond, integrating and customizing a wide range of machine learning techniques to solve complex problems in healthcare. By developing next-generation AI agents, algorithms, and computation engines, you will help Strata strengthen its market leadership, improve operational efficiency, and support healthcare providers in delivering high-quality care while maintaining financial health.

A Day in the Life

• Read and translate the latest research (e.g., arXiv papers) into production-ready solutions in Python.
• Prototype and iterate on machine learning models, focusing on areas such as regression, causal inference, optimization, and vector embeddings.
• Collaborate with cross-functional teams to embed ML and AI capabilities directly into our software platform.
• Partner with data scientists to design experiments and apply statistical concepts to real-world data.
• Optimize, test, and scale ML models to support mission-critical healthcare analytics.

Our Technology Stack

Our core platform is used by more than half of the nation’s leading healthcare providers, enabling them to leverage financial, operational, and clinical data. Our AI and ML stack includes:

• Languages & Libraries: Python, PyTorch, NumPy, Pandas, Polars, PyMC
• Infrastructure: AWS, Snowflake, Docker, GitHub
• Techniques & Tools:
  • Regression (with and without Bayesian priors)
  • Vector embeddings, similarity, clustering
  • Core statistics and distributions for EDA
  • Optimization methods (multi-armed bandit, mixed integer programming)
  • Causal inference and probabilistic modeling

What We’re Looking For

We’re seeking a technically curious engineer who thrives on turning theory into practice. The ideal candidate has:

• Strong experience implementing ML models in Python.
• Familiarity with regression, embeddings, causal inference, and optimization techniques.
• Experience applying statistical methods to exploratory data analysis.
• Comfort working with modern ML libraries and frameworks.

Bonus points if you have worked with:

• NLP tasks (LLMs, spaCy, neural networks).
• Recommender systems, latent factors, matrix factorization.
• Graph algorithms.
• Claude Code, Docker, and GitHub.ess computation engine.