Quantum Algorithm/Research Lead

Company Description QAI (Quantum AI) is a consulting and business development firm focused on converting advanced quantum research into practical, high-impact solutions for the financial sector. The company specializes in portfolio optimization, integrating quantum algorithms, state-of-the-art AI, and deep financial expertise to design superior investment strategies. QAI supports financial institutions, hedge funds, and wealth managers with solutions for complex risk modeling, asset allocation, and rapid dynamic rebalancing. Through strategic consulting and partnership models, QAI helps organizations adopt quantum-powered tools, build internal capabilities, and secure sustainable competitive advantages. The team is committed to bridging the gap between quantum research and real-world impact in today’s volatile markets.

Role Description As a Quantum Algorithm/Research Lead at QAI, you will drive the design, analysis, and implementation of quantum algorithms for portfolio optimization and related financial applications in an on-site, full-time role based in Singapore. You will lead research initiatives that integrate quantum computing, machine learning, and quantitative finance, translating theoretical advances into deployable prototypes and production-ready solutions. Day to day, you will formulate research directions, develop and test algorithms on quantum and classical simulators, collaborate with engineers to optimize performance, and validate results with financial domain experts. You will oversee and mentor a research team, review scientific outputs, and contribute to publications, white papers, and internal knowledge assets. You will also partner with business development and client teams to explain technical concepts, support pilot projects, and help shape the product and technology roadmap.

Qualifications

• Strong background in quantum computing and algorithms, including familiarity with variational algorithms, quantum optimization (e.g., QAOA, QUBO formulations), and quantum simulation.
• Solid foundation in mathematics and theoretical computer science, such as linear algebra, probability, optimization, and complexity theory.
• Proficiency in programming for scientific computing (e.g., Python) and experience with quantum SDKs or frameworks (such as Qiskit, Cirq, PennyLane, or similar).
• Experience in quantitative finance or financial engineering, particularly in portfolio optimization, risk modeling, or asset allocation, or strong interest and ability to learn these domains quickly.
• Demonstrated research experience, including leading projects, publishing in peer-reviewed venues, or delivering high-impact applied research in academia, industry, or a lab environment.
• Ability to translate complex technical concepts into clear narratives for non-technical stakeholders and collaborate effectively with cross-functional teams.
• Prior leadership experience, such as mentoring researchers or engineers, coordinating project timelines, and setting technical direction.
• Advanced degree (PhD or equivalent experience) in Physics, Computer Science, Mathematics, Engineering, or a related field with