Applied Research Scientist – Quantum-inspired and Randomized Algorithms - Sr. Associate

re you looking for an exciting opportunity to join a dynamic and growing team in a fast paced and challenging area? This is a unique opportunity for you to work with Global Technology Applied Research (GTAR) center at JPMorgan Chase & Co.. The goal of GTAR is to design and conduct research across multiple frontier technologies, in order to enable novel discoveries and inventions, and to inform and develop next-generation solutions for the firm’s clients and businesses.

We are looking for applied algorithms researchers with (i) strong publication record in quantum-inspired or randomized algorithms for optimization and machine learning and/or (ii) experience developing performant code, including using accelerators like GPUs. Experience in finance is a plus, though no prior familiarity with financial use cases is required.

Job Responsibilities:

We are looking for innovative problem solvers with a passion for advancing the state of the art of quantum-inspired algorithms. The focus of your role will be to:

• Advance the field of quantum-inspired and randomized algorithms and their applications to optimization, machine learning and financial use cases
• Implement the developed algorithms in performant software
• Provide novel research solutions to problems faced by internal project teams
• Work with other researchers to document your findings in scientific papers and present them at conferences
• Contribute to JPMC’s IP by pursuing necessary protections of generated IP

Required qualifications, capabilities, and skills

• Ph.D. degree in computer science, physics, math, engineering or related fields
• Demonstrated research ability in quantum-inspired algorithms or related fields
• Experience in scientific technical writing
• Proficiency in Python or C/C++
• Experience developing performant codes
• Strong communication skills and the ability to present findings to a non-technical audience
• Experience in one or more following domains: 
  • Quantum-inspired algorithms (e.g., dequantized algorithms, tensor networks, novel techniques for Ising solvers)
  • Randomized algorithms (e.g., streaming algorithms, data sketching techniques)
  • Architectural design for efficient implementation (e.g., developing memory efficient sampling techniques, end-to-end implementation of quantum-inspired and randomized algorithms)
  • GPU programming (e.g., CUDA, SYCL)

Preferred qualifications, capabilities, and skills

• Preference is given to candidates with strong publication record (example venues include but are not limited to ICML, NeurIPS, ICLR, ISCA, HPCA, STOC, FOCS)
• No prior familiarity with finance or financial use cases is required
• Preference given to candidates who includes a link to their Google Scholar or Semantic Scholar profile in their resume. 

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