Overview
As a Solutions Engineer at Quantum Dice, you will help turn probabilistic computing research into practical solutions for real-world industrial problems.
You will work across the full customer journey: understanding operational challenges, translating them into mathematical models, developing Python-based prototypes, benchmarking performance and supporting proof-of-concept delivery.
This is a technical, customer-facing role spanning the Solutions, Applied Research, Engineering and Commercial teams. The work focuses on hard optimisation and decision-making problems across sectors such as defence and national security, logistics and supply chain, telecommunications, energy and financial services. Some defence projects may be classified or export-controlled and will be delivered under the associated handling and security requirements.
You Will Work On
- Translate customer challenges, data and constraints into mathematical and computational models.
- Identify problems suited to probabilistic computing: optimisation, sampling, simulation, inference and decision support, including use cases such as mission planning, routing and scheduling, sensor and resource allocation, network planning, secure logistics and portfolio optimisation.
- Build Python prototypes on Quantum Dice’s simulators, software stack and PPU hardware, plus reusable code and solution templates.
- Apply QUBO, Ising models, Bayesian networks, Boltzmann machines, Monte Carlo and stochastic optimisation.
- Benchmark against classical solvers, CPUs and GPUs, and evaluate solution quality, runtime, scalability and commercial value.
- Deliver technical reports, demonstrations and proof-of-concepts, and support proposals, customer workshops and technical sales discussions.
- Work within relevant defence programme constraints, including security classification, export control, data handling and customer accreditation.
- Feed customer requirements back into Quantum Dice’s product, algorithm and hardware roadmaps.
Who You Are
- Technical degree in Physics, Computer Science, Applied Mathematics, Engineering or Operations Research.
- Strong Python skills and some experience in optimisation, probabilistic modelling, simulation or statistical inference.
- Able to turn open-ended business problems into structured technical models.
- Clear communicator who presents results honestly, without overstating them.
- Enjoys the commercial side as much as the modelling: working directly with customers in a fast-moving deep-tech environment.
Desirable
- MSc or PhD in a relevant discipline.
- Knowledge of QUBO, Ising models, Bayesian inference, MCMC or energy-based models.
- Experience with solvers such as Gurobi, CPLEX, OR-Tools or Pyomo, or stochastic methods such as simulated annealing.
- Customer-facing proof-of-concept delivery, ideally in a deep-tech startup.
- Domain experience in defence, aerospace, government, logistics, telecoms, energy or financial services.
Why Join Quantum Dice?
At Quantum Dice, we're building more than just hardware but creating a new category of computing.
You'll join a team of physicists, engineers, mathematicians and entrepreneurs working to bring probabilistic computing into real-world deployment. You will work directly with leading organisations, influence the future direction of the ORBIT
platform and help define how a fundamentally new computing architecture is applied to optimisation, decision-making and AI challenges in some of the world's most demanding operational environments.
Solutions Engineer in Oxford employer: Quantum Dice
As a leading innovator in the tech industry, we pride ourselves on fostering a collaborative and dynamic work environment that empowers our employees to excel. Our HaaS Program Lead role offers not only competitive benefits and opportunities for professional growth but also the chance to work at the forefront of hardware and cloud technology in a vibrant location. Join us to be part of a team that values creativity, encourages continuous learning, and is dedicated to delivering exceptional solutions for our enterprise customers.
