Full Stack Software Engineer – IBM Quantum

At IBM Research, we are the innovation engine of IBM. Exploring what’s next in computing and shaping the technologies the world will rely on tomorrow. From advancing AI and hybrid cloud to pioneering practical quantum computing, we anticipate challenges and unlock new opportunities for clients, partners, and society. Working in Research means joining a team that accelerates discovery at the intersection of high-performance computing, AI, quantum, and cloud. You’ll collaborate with leading scientists, engineers, and visionaries to push boundaries and turn ideas into reality. With a culture built on curiosity, creativity, and collaboration, IBM Research offers the opportunity to grow your career while contributing to breakthroughs that transform industries and change the world.

Your Role And Responsibilities

• Design and implement high‑performance, scalable Python code for compute‑intensive applications
• Analyze, refactor, and optimize large and complex existing Python codebases
• Develop and evaluate efficient algorithms and data structures
• Identify and resolve performance bottlenecks (CPU, memory, I/O) providing robust solutions
• Use profiling, benchmarking, and debugging tools to guide optimizations
• Collaborate closely with engineers, architects and researchers on performance‑critical components
• Ensure code quality, maintainability, and testability and drive adoption of best practices

Master's Degree

Required Technical And Professional Expertise

• Demonstrated optimization of Python libraries
• Proven expertise in performance‑critical Python programming and optimization
• Strong background in algorithms, data structures, and complexity analysis
• Experience working with large, legacy or long‑lived codebases
• C/C++ & Rust integrations or low‑level systems programming

• Experience with numerical computing or data‑intensive systems
• Familiarity with CI/CD pipelines
• Background in computer science, mathematics, physics, or a related field
• Hands‑on experience with performance techniques such as:
• Profiling and benchmarking (e.g., cProfile, line_profiler)
• Low‑level optimization (e.g., NumPy, Numba, Cython)
• Parallelism and concurrency (multiprocessing, multithreading, async programming)