Thermal Protection System Materials Informatics Engineer

Application close date:

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.

As part of a hardworking team of specialists, technicians, and engineers, you will be responsible for developing
** data‑driven models and workflows
** that accelerate the development of
** thermal protection system (TPS) materials and processes**. You will combine your knowledge of
** materials science
** with
** machine learning and data analysis
** to help:
* define materials and process design spaces,
* build surrogate models and prediction tools, and
* guide TPS material screening and down‑selection using data‑driven methods.

You will leverage your knowledge and expertise to curate materials data, develop and validate ML models, and work closely with M&P engineers to transition insights into TPS material and process development. The developed methods will be applied across multiple launch vehicles and test campaigns. You will share in the team’s impact on all aspects of reusable launch vehicles.

We are looking for someone to apply their technical expertise, curiosity, and commitment to quality to positively impact safe human spaceflight. Passion for our mission and vision is required!##
** Responsibilities:
** As a TPS Materials Informatics Engineer, you will:
* ** 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.##
*
* Qualifications:

**
* ** 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…