At a Glance
- Tasks: Develop and optimise physics-based AI models for real-world applications.
- Company: Join a venture-backed UK tech firm focused on cutting-edge AI solutions.
- Benefits: Competitive day rate, remote work, and opportunities for professional growth.
- Other info: Flexible role with potential for career advancement in a dynamic team.
- Why this job: Make a tangible impact in AI while working with innovative technologies.
- Qualifications: Strong experience in physics-based modelling and proficiency in Python.
Senior Applied AI Scientist Contract |Remote (UK) | Competitive day rate | SC clearance | Morela is proud to be supporting a venture-backed UK technology business building AI systems for highly regulated and security-sensitive environments.
They are standing up a brand new modelling and optimisation capability, and this is one of the first senior hires into it.
The work is physics-based performance modelling, and the optimisation that turns those models into actual decisions.
Concept through to robust, tested production code running against real data on a cloud platform.
It is hands-on and delivery-focused, and suits applied ML engineers who build and ship models just as well as people from a research background.
The role is deliberately shaped to flex: depending on your strengths you might focus on building and shipping the models, or take on more of the modelling and optimisation approach itself, inside a delivery-driven, highly autonomous, full-stack engineering team.
THE PROBLEM Physics-based prediction.
Models that predict how a system performs under varying environmental conditions, fed by meteorological forecast data, calibrated and validated against real data rather than left as a notebook.
Optimisation that bites.
Optimising configuration and resource allocation against modelled performance, including optimisation under uncertainty.
This is where the modelling becomes a decision.
Defensible outputs.
Results have to be correct, explainable and defensible under independent validation, benchmarked against reference cases.
Nothing here can be a black box.
WHAT YOU WILL DO Implement, calibrate and validate physics-based performance models, and build the pipelines around them covering data preparation, calibration, evaluation and deployment.
Implement optimisation methods, including optimisation under uncertainty, against modelled performance, and integrate meteorological forecast data into the models.
Evaluate and benchmark outputs against reference cases so results stand up to independent validation, and write secure, high-performance production Python that meets regulated-environment standards.
Build the decision-support outputs that surface results to operators, working closely with data, platform and product teams, and contribute to the technical development of the wider team.
WHAT YOU WILL NEED Strong experience implementing mathematical, physics-based or simulation models in production systems, not only in research code.
Fluent scientific Python, Num Py, Sci Py and similar, with solid software engineering fundamentals.
Experience with simulation, numerical methods or optimisation techniques, including optimisation under uncertainty.
A strong academic background in a quantitative field such as physics, applied mathematics or statistics, and the mathematical instinct to pick up an unfamiliar technical domain quickly.
A track record of taking modelling work from prototype through to tested, production-quality code.
SC clearance.
Active clearance is strongly preferred for this contract given programme timelines; for the right person, genuine eligibility will be considered.
WHAT WOULD HELP Signal processing, time-series or sensor-data modelling, advanced simulation techniques, and defence modelling and simulation or operational analysis experience.
Machine learning applied alongside physics-based modelling, such as surrogate models or calibration from operational data, and containerised deployment with Docker and Kubernetes.
Publications or a research background, decision-support or operational planning systems, and mentoring experience or the appetite to grow into it.
CONTRACT TERMS AND CLEARANCE Competitive day rate £500/£550per day, negotiable depending on experience and clearance status.
Active SC sits at the top of the range.
Remote across the UK, with occasional travel to client sites.
Duration and IR35 status confirmed on application.
Read this carefully.
SC eligibility requires a provable five-year UK address history.
If you have lived outside the UK within that window, this will be difficult on this programme's timeline.
A DBS check or BPSS clearance is not the same as SC.
For a confidential conversation and the client name, email adam. moore@morela. co. uk
Senior Applied AI Engineer - Contract employer: Morela Solutions
Morela's client is an exceptional employer, offering a dynamic and collaborative work environment in the heart of the City of London. With a strong focus on employee growth, they provide comprehensive benefits including private medical insurance, generous holiday allowances, and opportunities for professional development, all while fostering a culture that values curiosity and ownership. This scale-up not only empowers its engineers to make a tangible impact through client-facing roles but also ensures a supportive atmosphere with regular team socials and a commitment to work-life balance.
