Thermal Protection System Materials Informatics Engineer

Thermal Protection System Materials Informatics Engineer

Applications will be accepted on an ongoing basis until the requisition is closed.

At Blue Origin, we envision millions of people living and working in space for the benefit of Earth. We’re working to develop reusable, safe, and low-cost space vehicles and systems within a culture of safety, collaboration, and inclusion. Join our team of problem solvers as we add new chapters to the history of spaceflight! This role supports the development and operations of New Glenn, a single‑configuration, heavy‑lift orbital launch vehicle capable of routinely carrying people and payloads to low‑Earth orbit, geostationary transfer orbit, cislunar, and beyond.

Its first stage is fully reusable, and the vehicle was designed from the beginning to be human‑capable.

• Develop and apply data‑driven models
  • Build and evaluate surrogate models (e.g., regression, Gaussian Processes, neural networks) to predict key TPS material properties and performance metrics from composition, processing, and test conditions.
  • Support implementation of Bayesian optimization or related methods to propose new material compositions, processes, or test conditions.
• Curate and analyze materials data
  • Collect, clean, and organize TPS materials data from experiments, simulations, and literature into structured datasets suitable for analysis and ML.
  • Perform exploratory data analysis to identify trends, correlations, and data gaps that inform future testing and development plans.
• Integrate domain knowledge into models
  • Work with TPS materials and process engineers to translate known design rules, constraints, and operating environments (e.g., re‑entry conditions) into model features, priors, and search‑space constraints.
  • Help encode empirical and physics‑based relationships (e.g., degradation mechanisms, thermal response trends) into models and analysis workflows.
• Collaborate and communicate
  • Partner with experimental and manufacturing teams to design data‑driven screening and development plans, interpret model results, and incorporate new data into the modeling workflows.
  • Prepare clear plots, reports, and presentations to communicate findings and recommendations to cross‑functional stakeholders.
• Continuously improve tools and methods
  • Maintain and improve analysis scripts, Jupyter notebooks, and related tools; follow good software practices (version control, documentation).
  • Stay current with developments in materials informatics and ML for scientific discovery and help bring relevant methods into the TPS development workflow.

• Education
  • Bachelor’s degree in Materials Science and Engineering or other relevant fields (e.g., Ceramic Engineering, Metallurgical Engineering, Mechanical Engineering, Chemical Engineering, Applied Physics).
  • OR Bachelor’s degree in Computer Science, Data Science, Electrical Engineering, or related field with demonstrated exposure to materials or physical sciences.
• Technical – Materials
  • Familiarity with structure–processing–property relationships in materials.
  • Exposure to high‑temperature materials, thermal protection systems, ceramics, composites, or related domains through coursework, projects, research, or internships.
  • Ability to read and interpret technical materials literature (e.g., property data, degradation mechanisms, processing–microstructure relationships).
• Technical – Data & Machine Learning
  • Proficiency in Python for scientific computing and data analysis (e.g., Num Py, pandas, matplotlib).
  • Hands‑on experience with at least one machine learning or data‑analysis library (e.g., scikit‑learn, PyTorch, Tensor Flow).
  • Working understanding of basic ML concepts: regression, training/validation splits, overfitting/underfitting, and model evaluation metrics.
• Team & Culture
  • Strong written and verbal communication skills; ability to explain technical topics to audiences.
• Prerequisite knowledge or expertise in at least one of the following areas
  • Application of machine learning or advanced data analysis to materials, chemistry, or other physical science problems.
  • High‑temperature materials or TPS materials and their performance/degradation mechanisms.
  • Handling and…