Job Description
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 highly motivated AI/ML Engineer to join our team for a groundbreaking project on AI-assisted structural optimization and material selection for defense applications. In this role, you will work closely with internal AI/ML experts developing advanced AI workflows. This workflow integrates topology optimization, finite element analysis (FEA), materials selection, and manufacturability constraints to accelerate and automate the redesign of critical components for improved performance and cost efficiency. The ideal candidate combines hands-on experience with FEA and optimization tools with strong engineering judgement around materials and manufacturing feasibility.
Key Responsibilities
- Analyze and optimize vehicle components
- Perform finite element analysis (FEA) and topology optimization using tools such as Altair or ANSYS, and evaluate results in the context of material and manufacturing constraints
- Guide materials selection and substitution efforts for component redesign, using commercial alloy databases and engineering tradeoffs
- Support the integration of AI/ML models with FEA and topology optimization workflows
- Evaluate and refine designs for manufacturability, considering traditional processes (e.g., casting, forging, welding)
- Provide knowledge on metals, metal alloys, their processing conditions, and manufacturing methods
- Document workflows, tools, and deliverables to support handoff and commercial readiness
- Participate in internal and client-facing meetings, presenting technical findings clearly and effectively
Required Qualifications
- MS or PhD in Mechanical Engineering, Materials Engineering, or related field
- Strong experience (5+ years) in at least two of the following areas:
- Finite Element Analysis (e.g., Altair, ANSYS)
- Topology optimization methods and tools
- Applied materials engineering or material selection for manufacturable component design
- Familiarity with CAE procedures and target requirements for vehicle components
- Ability to evaluate manufacturability (e.g., forging, casting, joining) and material-performance tradeoffs
- Proficiency in Python and familiarity with APIs for engineering software integration
- Excellent problem-solving and communication skills
Preferred Qualifications
- Background in metallurgical engineering or applied materials science
- Experience supporting or developing AI-driven tools for engineering or manufacturing workflows
- Understanding in structural engineering or lightweight design principles
- Familiarity with cloud-based high-performance computing environments
The US base salary range for this full-time position is expected to be $179k-$251k 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.
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