Blue Origin-posted 4 days ago
Full-time • Entry Level
Seattle, WA
5,001-10,000 employees

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!

  • 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.
  • 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 .
  • 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).
  • Proficiency in Python for scientific computing and data analysis (e.g., numpy, pandas, matplotlib or similar).
  • Hands‑on experience with at least one machine learning or data‑analysis library (e.g., scikit‑learn , PyTorch , TensorFlow , or similar) from coursework, projects, or research.
  • Working understanding of basic ML concepts: regression, training/validation splits, overfitting/underfitting, and model evaluation metrics.
  • Strong written and verbal communication skills; ability to explain technical topics to audiences.
  • You need to have in‑depth 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 interpreting complex experimental or simulation datasets (e.g., multi‑parameter test matrices, time‑dependent response data, or multi‑fidelity data from experiments and modeling).
  • Advanced degree (M.S. or Ph.D., completed or in progress) in Materials Science & Engineering or related field, particularly with a focus on materials informatics or data‑driven materials design .
  • Hands‑on experience with:
  • Gaussian Process Regression or other probabilistic models.
  • Bayesian optimization, active learning, or sequential design of experiments.
  • Neural network models (e.g., feed‑forward NNs, graph neural networks, or physics‑informed neural networks) for surrogate modeling of physical systems.
  • Experience with:
  • TPS materials screening, down‑selection, or development for re‑entry or similar extreme environments.
  • Materials modeling tools or data (e.g., finite element thermal/structural models, DFT/MD/CALPHAD outputs).
  • Familiarity with:
  • Version control (e.g., Git) and collaborative code development.
  • Jupyter notebooks or similar tools for reproducible analysis and reporting.
  • Linux/HPC environments or GPU‑accelerated workflows.
  • Hands‑on experience taking a materials or process concept from ideation through test and into hardware (e.g., senior design projects, research, or prior roles).
  • Medical, dental, vision, basic and supplemental life insurance, paid parental leave, short and long-term disability, 401(k) with a company match of up to 5%, and an Education Support Program.
  • Paid Time Off: Up to four (4) weeks per year based on weekly scheduled hours, and up to 14 company-paid holidays.
  • Dependent on role type and job level, employees may be eligible for benefits and bonuses based on the company's intent to reward individual contributions and enable them to share in the company's results, or other factors at the company's sole discretion.
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