About Us

At Gridcog, we’re on a mission to accelerate investment in the clean energy transition. We are a SaaS start-up of energy nerds building the best project modelling software to enable our users to make intelligent energy decisions.

Who we’re looking for

We’re looking for a talented Simulation Engineer who thrives in ambiguity and is motivated by solving complicated, multi-faceted business problems with a holistic view of the solution.

The Gridcog platform core capability is modelling the physical and commercial behaviour of energy projects integrating solar, wind, batteries, flexible loads, gensets, EV fleets, and their interactions with electricity markets and tariff structures all around the world.

Ideally you have some experience with energy systems modelling, but you may have transferable experience from other modelling or data science domains.

You will help evolve and extend the simulation and optimisation capabilities of our Python codebase, working across mathematical modelling, numerical methods, and production software engineering to deliver accurate, performant, and reliable modelling at scale.

Our ideal candidate has:

• Strong problem-solving and analytical skills, with the ability to reason about complex systems and translate real-world energy problems into mathematical models.
• Deep and current experience with Python in a scientific computing or simulation context, including libraries such as numpy, scipy, pandas, and polars.
• Willing to take responsibility and ownership over key features and systems, with a foundation in rigorously validating assumptions and providing clarity when moving from hypothesis to proof.
• A quality mindset where testing is a key part of the work, and expertise in hard numerical debugging and isolating edge cases.  You build tested, maintainable, well-structured systems that will run reliably in production.
• Comfort with numerical methods and working with large volumes of time series data, including understanding of numerical stability, precision, and performance trade-offs.
• A solid foundation in the mathematics and physics relevant to energy system modelling — for example, energy balance, power flows, battery degradation, time-of-use economics, or similar domains involving applied maths and physical constraints.
• Experience with mathematical optimisation desirable — formulating and solving problems using techniques such as linear programming (LP), mixed-integer linear programming (MILP). Familiarity with solvers or modelling frameworks such as PuLP, Gurobi, CPLEX, OR-Tools, and similar.
• Experience with automated testing of complex, stateful systems — including strategies for validating simulation outputs and handling floating-point comparisons.
• Experience with data visualisation tools and techniques
• Solid foundation in software design, data structures and algorithms.
• Ability to work collaboratively in both in-person and remote work environments.
• Ability to communicate technical concepts clearly — including explaining modelling assumptions, trade-offs and limitations to both technical and non-technical team members.
• Experience with version control, CI/CD, and modern development practices.

A background in energy systems, power engineering, physics, applied mathematics, operations research, or scientific computing is advantageous. Candidates with a research background, such as a Maths or Physics PhD, who are looking to transition to production-focused engineering are highly encouraged to apply. A growth mind-set, experience with startup SaaS, and an interest in the energy system transition all greatly beneficial.

What you’ll do:

• Build and take ownership of key components of our simulation engine, extending the range of energy assets, market structures, and optimisation strategies our platform can model.
• Design and implement mathematical models of physical and commercial energy system behaviour, translating real-world complexity into robust, performant code.
• Formulate and solve optimisation problems to help users identify the best configurations, operating strategies, and investment decisions for their energy projects.
• Work with large volumes of time series data — market prices, generation profiles, demand curves, weather data — as inputs to and outputs from simulation runs.
• Improve the accuracy, performance, and scalability of simulation workloads, including profiling, algorithmic improvements, and appropriate use of parallelism.
• Contribute to the design of our simulation architecture — how models compose, how scenarios are structured, and how results are computed and stored.
• Troubleshoot and resolve issues in simulation outputs, including debugging numerical edge cases and validating results against real-world data.
• Write clear documentation of modelling assumptions, algorithms, and design decisions.
• Collaborate with product managers, energy analysts, and other engineers to understand customer needs and translate them into modelling capabilities.
• Our business works across the UK, Europe, and Australia, covering 10 hours of timezones - so some working time flexibility and effective async collaboration will be required.

Benefits:

• Competitive remuneration package aligned with experience and skills
• Join a diverse start-up in the energy space and be part of a high-performing team that values innovation and creative problem-solving
• We will help build your personal profile in the energy sector via sharing your insights and contributions on our company LinkedIn page
• Contribute to the decarbonisation of the world’s energy system
• Support for ongoing professional and personal development
• Best possible tools (choice of laptop, home office & tech budget, Gurobi, best available AI tools)
• An annual all company retreat to have our colleagues from UK, Europe and Australia all in one place – last year we went to Bintan, Indonesia

We are committed to building a diverse and inclusive team at Gridcog. We welcome applicants from all backgrounds, as we believe an inclusive environment and diversity of perspective leads to innovation and success.