Job Description

About Applied Computing

Applied Computing builds foundation models for the most valuable and consequential sectors on the planet, all within the energy chain. We came out of stealth less than a year ago and have already seen exceptional success. We're now looking to grow our team with world-class minds who are driven to grow into the best version of themselves and to put their shoulder behind the wheel of applied AI, so we're recruiting a AI Researcher with specialism in Time Series.

The Role

Our foundational time series engine blends classical time-series techniques with deep neural networks that respect the underlying physics of complex systems like refining process control.




You'll own the end-to-end development of this core engine--from research experiments all the way to real-time model inference in production.




Expect to tackle everything from probabilistic model design and uncertainty quantification to scalable deployment pipelines and continuous retraining workflows.

Key Responsibilities

Design & Implement Time Series Models

1 foundation time series model for 3 tasks: classification, forecasting, optimisation.

Experiment with various loss functions from generative modelling to reinforcement learning (your design, your call), with physics-based constraints.

Develop dual-model frameworks that blend statistical and deep-learning approaches to quantify uncertainty and improve robustness.

Embed Physics-Informed Constraints

Integrate domain physics (e.g., conservation laws, differential equations) into neural architectures to enhance generalisation and interpretability.

Optimise Production Workflows

Containerise models (Docker) and deploy on AWS (e.g. ECS/EKS/SageMaker).

Build CI/CD for model training, evaluation, and rollout with automated retraining triggers.

Benchmark & Validate

Establish rigorous automated scripts for evaluation metrics and back-testing protocols.

Must Have

Exceptional organisation and time management.


PhD in Computer Science, Statistics, Applied Mathematics, Physics, or related.

Publications as first author in time series modelling, forecasting, signal processing, or physics-informed ML.

Familiarity with feature stores, model registries, and MLOps frameworks (e.g., MLflow).

3+ years hands-on experience in time-series forecasting and deep-learning research.

Expert in Python and ML frameworks (PyTorch specifically), with production-grade code quality.

Strong Foundation in probabilistic modeling, Bayesian methods, and uncertainty quantification.

Production Skills: Docker, container orchestration, AWS / Azure services for deep learning training.
Communication: Ability to document methodologies in research paper format and present findings to technical and non-technical stakeholders.

An Insight into Culture

We don't just hire for skills -- we hire for trajectory.

Whether you're an AI researcher, machine learning engineer, software engineer, GTM strategist or operations builder, we're looking for people who raise the bar. Not just technically, but in how you communicate, lead, and execute. If you've ever felt underused, under-challenged, or stuck in slow-moving / bogged down teams and projects -- we are your reset button.



We write our own rules.


We care more about your ambitions than your CV.
We hire engineers who ship, AI researchers who bring real world impact, and commercial talent who think like entrepreneurs.


Finally, if you are highly autonomous, low-ego, and aligned with Applied Computing's mission come talk to us.