We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.

As a Lead Software Engineer at JPMorgan Chase within the Chief Technology Office’s Global Technology & Applied Research team, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. You work in close collaboration with the quantum algorithms research team.

Job responsibilities 

Lead quantum software projects Develop and maintain software implementing quantum algorithms and integrating them with error correction and fault tolerance Work with quantum algorithm researchers to support their research efforts by providing reliable software implementations of algorithms Work with quantum error-correction researchers to develop and maintain error-correction research software, and integrate algorithms with error correction across hardware platforms Develop software to support experiments on quantum hardware Contribute to JPMC’s IP by pursuing necessary protections of generated IP

Required qualifications, capabilities, and skills

Formal training or certification on software engineering concepts and 5+ years applied experience Bachelor’s degree combined with 4+ years of quantum computing industry experience or Master’s or Ph.D. degree in computer science, physics, math, engineering or related fields combined with 2+ years of quantum computing industry experience Demonstrated ability to maintain or develop to quantum computing software Proficiency in Python Experience developing of quantum software (e.g., Qiskit, PennyLane, Cirq) Experience leading software projects

Preferred qualifications, capabilities, and skills

Proficiency in C / C++ / Rust / Julia Experience implementing quantum algorithms for optimization (e.g., QAOA, quantum adiabatic algorithm, quantum walks) Experience implementing quantum algorithms for machine learning (e.g., quantum algorithms for linear systems, PCA, classification) Experience implementing quantum linear algebra (e.g., LCU, QSVT) Experience in compilation of quantum algorithms to fault-tolerant architectures Experience in simulation of quantum algorithms (e.g., MPS, PEPS, tensor networks) Experience in finance is a plus, though no prior familiarity with financial use cases is required.