Quantum AI Engineer at Stealth Post-LLM Startup Quantum AI Engineer at Stealth Post-LLM Startup in San Lorenzo, California Posted in 1 day ago.

Type:

full-time

Quantum AI Engineer Location:

Los Altos, CA | On-site /

Hybrid Employment Type:

Full-Time About Dyssonance Dyssonance is building a new class of cognitive infrastructure for artificial intelligence. We are working on fundamental problems in reasoning, uncertainty, learning, planning, and intelligent systems , developing architectures that allow AI systems to maintain and update internal representations of the world, reason under uncertainty, and adapt as information changes. Our team brings together former DeepMind, Google, and Apple researchers and engineers, alongside PhD-level researchers across AI, mathematics, physics, neuroscience, and related fields . We are a small, deeply technical team working at the boundary between fundamental research and real-world AI systems. We are now expanding our exploration of quantum and quantum-inspired approaches to artificial intelligence and are looking for an exceptional Quantum AI Engineer to help build that capability. About the Role As a Quantum AI Engineer, you will work at the intersection of quantum computing, artificial intelligence, and research engineering . This is not primarily a quantum hardware role, nor is it a purely theoretical research position. You will work closely with our AI researchers to identify where quantum and quantum-inspired computational methods could meaningfully extend our approaches to reasoning, inference, optimization, learning, and representation, and then build the experiments required to determine whether they actually work. You should be equally comfortable discussing an algorithm on a whiteboard and implementing it. We are looking for someone who can move from paper to mathematical formulation to implementation to experiment to evidence quickly and independently. What You'll Do Explore applications of quantum and quantum-inspired computation to fundamental AI problems, including reasoning, inference, optimization, search, learning, and representation. Translate mathematical and research ideas into rigorous, executable implementations. Implement and evaluate quantum algorithms using modern quantum-computing frameworks and simulators. Design hybrid quantum-classical approaches and determine where quantum components provide meaningful computational or representational advantages. Build simulation, experimentation, and benchmarking infrastructure for quantum and quantum-inspired systems. Develop rigorous comparisons between quantum, quantum-inspired, and conventional classical approaches. Investigate computational complexity, scaling behavior, noise sensitivity, trainability, and practical resource requirements. Implement optimization, sampling, probabilistic inference, and linear-algebra-intensive algorithms. Work with CPU, GPU, simulator, and available quantum-computing environments as appropriate. Reproduce and critically evaluate results from the quantum computing and quantum AI literature. Profile and optimize computationally intensive experiments. Build tools that shorten the cycle between hypothesis, implementation, experiment, and result. Collaborate directly with researchers across AI, mathematics, physics, neuroscience, and engineering. Contribute to research prototypes that may ultimately become components of production AI systems. Maintain high standards for code quality, testing, reproducibility, experimental design, and documentation. What We're Looking For Strong engineering ability is essential. We are not looking for someone who can only formulate interesting quantum algorithms and hand their implementation to somebody else. You should be capable of independently turning research ideas into working experimental systems.

You likely have:

A MS or PhD in Physics, Mathematics, Electrical Engineering, Quantum Information , Computer Science, or another highly quantitative discipline. Strong proficiency in Python (at least 3 years of experience) and experience building substantial scientific, ML, or technical software. A strong understanding of quantum computing fundamentals, including quantum states, circuits, gates, measurement, entanglement, and quantum algorithms. Experience with one or more quantum-computing frameworks such as Qiskit, Cirq, PennyLane, CUDA-Q , or equivalent systems. Strong foundations in linear algebra, probability, optimization, numerical methods, and algorithms . Familiarity with modern machine learning and AI techniques. The ability to read mathematical or theoretical research and translate it into working implementations. Experience designing experiments capable of validating or falsifying technical hypotheses. Strong debugging skills across algorithms, numerical computation, software, and experimental infrastructure. The ability to work autonomously while collaborating closely with a multidisciplinary research team. Particularly Interesting to Us We would be especially interested in experience with one or more of: Quantum machine learning Variational quantum algorithms Quantum optimization Quantum sampling Quantum search Hamiltonian simulation Tensor networks Quantum-inspired classical algorithms Hybrid quantum-classical systems Probabilistic inference Graph algorithms Combinatorial optimization Representation learning Reinforcement learning GPU-accelerated scientific computing Large-scale quantum simulation Quantum error mitigation Quantum compilation and intermediate representations Benchmarking quantum advantage or quantum utility Fundamental connections between information, computation, and intelligence Direct experience with every area above is not expected. Intellectual range and the ability to learn unfamiliar technical domains quickly matter more. How We Work Dyssonance operates differently from a traditional research lab. We care deeply about theoretical understanding, but research must ultimately encounter evidence . We expect researchers and engineers to shorten the distance between an idea and the experiment that can validate or invalidate it. If an experimental loop is too slow, we improve the loop. If infrastructure is blocking an answer, we build better infrastructure. If an assumption can be tested, we test it. We value people who can challenge an approach rigorously (including their own) and who are comfortable discovering that an elegant idea doesn't work. A successful Quantum AI Engineer at Dyssonance can encounter an unfamiliar research problem, understand the relevant mathematics, formulate a computational approach, build a credible experiment, and produce evidence that changes what the team believes. You Might Be a Great Fit If You are the kind of person who reads a theoretical paper and immediately starts thinking about how you would test it . You enjoy mathematics and first-principles reasoning, but you also love building things. You can move comfortably between abstraction and implementation. You don't need a perfectly specified problem before beginning. You can identify what you don't know, ask the right questions, build the smallest experiment that resolves the uncertainty, and iterate from there. You are capable of working independently without becoming intellectually isolated . You actively seek context from the broader system, collaborate with other researchers, and understand how your work connects to the team's larger technical objectives. And you are excited by problems where the answer may not exist yet. Why Dyssonance You'll join a small, unusually multidisciplinary technical team of former DeepMind, Google, and Apple researchers and engineers and PhD-level scientists working on fundamental questions about intelligence and computation. You'll have the opportunity to explore quantum AI without being constrained to a predetermined thesis about where quantum computing should help. We care about finding out where it actually does . You'll work directly with the people developing Dyssonance's core AI architecture and have significant ownership over the company's quantum-computing research and engineering direction. This is a role for someone who wants to build a new technical area, not inherit an established one.