About SandboxAQ
SandboxAQ is a high-growth company delivering AI solutions that address some of the world's greatest challenges. The company’s Large Quantitative Models (LQMs) power advances in life sciences, financial services, navigation, cybersecurity, and other sectors.
We are a global team that is tech-focused and includes experts in AI, chemistry, cybersecurity, physics, mathematics, medicine, engineering, and other specialties. The company emerged from Alphabet Inc. as an independent, growth capital-backed company in 2022, funded by leading investors and supported by a braintrust of industry leaders.
At SandboxAQ, we’ve cultivated an environment that encourages creativity, collaboration, and impact. By investing deeply in our people, we’re building a thriving, global workforce poised to tackle the world's epic challenges. Join us to advance your career in pursuit of an inspiring mission, in a community of like-minded people who value entrepreneurialism, ownership, and transformative impact.
About the Role
We are seeking a motivated postdoctoral researcher to develop and apply machine learning methods at the intersection of materials science and structural design. The position emphasizes data-driven approaches, including large language models (LLMs), reasoning frameworks, and Bayesian optimization, to support the identification of high-impact design opportunities, the development of lightweighting strategies, and decision-making under complex constraints. Working knowledge of alloys will help connect machine learning outputs to real-world design and manufacturability tradeoffs. In this role, you will advance ML methods that integrate diverse datasets, guide design choices, and accelerate engineering workflows. You will also have opportunities to extend these methods to alloy and material related challenges across our broader portfolio.
What You'll Do
- Develop and apply ML and optimization techniques to guide lightweighting strategies.
- Use reasoning-based ML approaches to evaluate trade-offs among performance, manufacturability and other criteria.
- Apply Bayesian optimization and related uncertainty-aware methods to balance performance, manufacturability, and other constraints.
- Build reproducible workflows that integrate materials data, manufacturing methods, and simulation outputs.
- Curate and analyze structured datasets on materials, processing routes, and mechanical properties to support ML pipelines.
- Collaborate with engineers and computer scientists to connect ML outputs with structural and materials design tasks.
- Write technical reports and present results to technical and non-technical stakeholders.
About You
- U.S. citizenship is required due to USG contract requirements.
- PhD in Materials Science, Metallurgy, Mechanical Engineering, Computational Materials Science, Applied Physics , or a related field.
- Demonstrated experience applying ML or statistical methods to materials or engineering applications.
- Proficiency in Python and ML frameworks (PyTorch, TensorFLow, Scikit-learn).
- Familiarity with optimization and uncertainty quantification methods such as Bayesian optimization, Gaussian processes, ensemble learning, or related approaches.
- Strong research track record, evidenced by publications in materials science, ML, or computational design.
- Excellent problem-solving and communication skills.
Nice to Have
- Familiarity with knowledge graphs or graph-based ML for materials/manufacturing data.
- Experience with LLMs for data integration, retrieval-augmented reasoning, or decision support.
- Experience with graph-based, generative, or physics-informed ML for materials or engineering applications.
- Background in lightweighting, alloy substitutions, or design for manufacturability.
- Experience working with experimental or simulation-based datasets in materials (e.g., thermomechanical processing data, microstructure characterization, or finite element modeling).
- Ability to work collaboratively in multidisciplinary teams.
The US base salary range for this full-time position is expected to be $115k - $125k per year. Our salary ranges are determined by role and level. Within the range, individual pay is determined by factors including job-related skills, experience, and relevant education or training. This role may be eligible for annual discretionary bonuses and equity.