Optimization Application Engineer

BQP is building the next-generation simulation platform,
BQPhy®, designed to solve the most complex computational challenges in aerospace, space, and defense. The platform integrates advanced solvers with proprietary quantum-inspired algorithms, delivering performance beyond the capabilities of modern GPUs. Running on classical high-performance computing systems, BQPhy® has demonstrated
up to 10X computational advantages
for aerospace and defense clients. The platform is built to transition seamlessly to quantum-native hardware as it matures, enabling sustained technical superiority and reduced development costs across industries.

We are seeking an Optimization Application Engineer with strongexpertisein Multidisciplinary Design Optimization (MDO) and a solid foundation in general optimization methods to work on complex, large-scale engineering problems in theAutomotiveand

Aerospace domain.

This role focuses on research, development, and application of advanced optimization methodologies for tightly coupled multidisciplinary systems involving high dimensionality, nonlinear constraints, and multiple competingobjectives.

The ideal candidate combines theoretical depth, algorithmicexpertise, and practical modeling skills, and is comfortable working at the intersection of mathematics, computation, and engineering systems.

• Formulate and solve large-scale, constrained, multi-objective optimization problems for space and aerospace systems.
• Design, implement, and benchmark optimization algorithms (gradient-based, gradient-free, evolutionary, hybrid approaches).
• Integrate and couple discipline-level models (structures, dynamics, propulsion, thermal, controls, etc.) into system-level optimization frameworks.
• Lead research and development of MDO formulations and architectures (e.g., MDF, IDF, CO,BLISSand variants).
• Conduct trade-off studies, sensitivity analyses, and uncertainty-aware optimization.
• Explore emerging optimization paradigms (surrogate modeling, ML-assisted optimization, quantum-inspired methods).
• Publish research findings and contribute to technical reports, white papers, and intellectual property.
• Collaborate with cross-disciplinary teams to translate research outcomes into deployable engineering solutions.

• 3+years' experience in Mechanical Engineering,Aerospace Engineering,Applied Mathematics, Operations Research, or a closely related field(OR)
  Master’sdegree in the above fields with strong research experience (peer-reviewed publications, thesis in MDO/designoptimization, or equivalent industrial research experience).
• Strong theoretical and practical knowledge of Multidisciplinary Design Optimization (MDO).
• Deep understanding of optimization theory and algorithms, including:
• Nonlinear and constrained optimization
• Multi-objective optimization
• Gradient-based and gradient-free methods
• Proven experience in mathematical problem formulation for complex engineering systems.
• Proficiency in
  
  MATLAB,Python for research-grade optimization and modeling.
• Hands-on experience with optimization and MDO frameworks/tools
• (e.g.,OpenMDAO,Pyomo, CasADi, Sci Py, or custom solvers).
• Strong numerical methods, linear algebra, and scientific computing skills.

• Domain experience in space or aerospace systems (mission design, structures, dynamics, propulsion, thermal systems).
• Experience with surrogate models, multi-fidelity optimization, or uncertainty quantification.
• Familiarity with high-performance computing, parallel optimization, or large-scale simulations.
• Background in quantum-inspired optimization, meta heuristics, or hybrid AI–optimization approaches.
• Track recordof peer-reviewed publications in optimization, MDO, or aerospace systems.