Research Engineer – Optimization

4 weeks ago

Bengaluru, Karnataka, 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.

QpiAI Quantum hardware team is responsible for designing and characterisation of Quantum

Processor, Cryogenic Quantum Control Circuits, RF Control Hardware, and QpiAI ASGP.

About the Role

We are building high-performance optimization infrastructure for real-world decision-making problems across logistics, manufacturing, and emerging tech domains. We are looking for a mathematically inclined, algorithmically sharp engineer who thrives at the intersection of theory and systems — someone who can translate abstract optimization problems into efficient, production-ready solvers.

Key Responsibilities

  • Design and implement fast, scalable solvers for complex optimization problems across discrete and continuous domains.
  • Develop constraint modeling frameworks and metaheuristic algorithms grounded in strong mathematical principles.
  • Evaluate solution quality, convergence behavior, and performance benchmarks across diverse instances and datasets.
  • Work with system engineers to integrate your solver modules into larger optimization stacks and real-time decision systems.
  • Explore and adapt techniques from mathematical programming, stochastic methods, and quantum-inspired approaches.

What We're Looking For

  • 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); low-level optimizations and profiler-driven development are a plus.
  • Experience with algorithm design for constraint systems, heuristics, and metaheuristics.
  • Hands-on coding profile: A high rating on platforms like Leetcode, Codeforces, or Hackerrank is a strong signal.
  • Product thinking: capable of modular, reusable, and extensible solver design; understands how solvers scale in production systems.
  • Exposure to quantum computing or hybrid quantum-classical paradigms is a plus.

Good to Have

  • Familiarity with model encoding techniques and constraint representations.
  • Benchmarking experience on large-scale combinatorial datasets.
  • Participation in mathematical modeling competitions (e.g., INMO, COMAP, Kaggle competitions involving optimization).

Why Join Us

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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