Research and Technology Engineer

1 week ago

Bangalore, India QpiAI Full time

About QpiAI At QpiAI, we are leading the effort to discover optimal AI and Quantum systems in Life sciences, Healthcare, Transportation, Finance, Industrial, and Space technologies. QpiAI is building a full- stack Enterprise Quantum Computers. We are building high-performance optimization infrastructure for real-world decision-making problems across logistics, manufacturing, and emerging tech domains. Evaluate solution quality, convergence behavior, and performance benchmarks across diverse instances and datasets. Explore and adapt techniques from mathematical programming, stochastic methods, and quantum-inspired approaches. Strong foundation in mathematics : linear algebra, combinatorics, graph theory, numerical methods, convex and discrete optimization. Algorithmic and systems thinking : should be able to write fast, memory-efficient code and optimize for performance bottlenecks. Exceptional programming skills in Python and C++ (or Rust/Julia); Experience with algorithm design for constraint systems, heuristics, and metaheuristics. Hands-on coding profile : Exposure to quantum computing or hybrid quantum-classical paradigms is a plus. Benchmarking experience on large-scale combinatorial datasets. Optimization Research Engineer - Screening Problems results against the Toshiba Digital Annealer results published on GSET instances. Implement a solver (Python, D-Wave, or simulated annealing). Solution Methodology Research: - Evaluate solver performance with respect to accuracy and computational time. Problem 2: Vehicle Routing with Time Windows (Medium) travel distance while satisfying delivery constraints. You must generate your own dataset with the following fields: - Demand (units) - Formulate the Vehicle Routing Problem with Time Windows (VRPTW) as a MILP. Generate synthetic datasets and demonstrate solution performance. Explain how constraints like capacity and delivery windows are modeled. Solution Methodology Research: - Discuss scalability and solver performance for varying problem sizes. Design and implement a custom Mixed Integer Linear Programming (MILP) solver from scratch. performance with commercial solvers such as Gurobi, CPLEX, or HiGHS in terms of runtime, Design and implement a modular MILP solver (Python or C++ preferred). Document solver architecture, algorithms, and implementation details. Produce a technical report detailing performance, comparisons, and insights. Solution Methodology Research: - Review state-of-the-art MILP solver architectures and compare with your implementation. - Suggest future improvements such as hybrid heuristics or parallelization. You’ll work closely with a team that understands optimization not just as a mathematical challenge, but as a product engineering problem. If you're excited about building fast solvers, pushing boundaries in hybrid or hardware-accelerated optimization, and solving problems at scale, we’d love to hear from you.

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