JPMorgan Applied Research Scientist – Quantum-inspired Randomized 

United States, New York, New York 

663699693

As an Applied Research Scientist – Quantum-inspired and Randomized Algorithms - Executive Director within the Global Technology Applied Research (GTAR) center at JPMorgan Chase & Co., you will be responsible for advancing the field of quantum-inspired and randomized algorithms and their applications to optimization, machine learning and financial use cases. You will also implement the developed algorithms in performant software, provide novel research solutions to problems faced by internal project teams, and contribute to JPMC’s IP by pursuing necessary protections of generated IP.

Job Responsibilities:

• Looking for innovative problem solvers with a passion for advancing the state of the art of quantum-inspired algorithms.
• 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, plus at least 6 years of experience (industry or postdoc)
• 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.