Graduate Research Engineer (Machine Learning, Operations & Optimisation)
Company Overview
We are a founder‑funded, research‑led AI start‑up headquartered in the Advanced Technology Innovation Centre (ATIC) at Loughborough University. Operating an agency model to fund the buildout of our next‑generation AI platform, we serve a broad portfolio of sectors including manufacturing, finance, entertainment, and healthcare while preparing to launch an ERP‑scale platform in 2026.
Our multidisciplinary research team, comprising PhD and fellowship researchers with close ties to regional universities, translates cutting‑edge deep‑learning and operations research into real‑world solutions.
We are on the look out for a research engineer to support, and be mentored by, our Chief Scientist.
The candidate will thrive on lateral thinking, collaboration, and getting ideas from whitepaper to whiteboard to working code.
Role Summary
We are seeking a graduate‑level Engineer / Researcher who combines strong ML fundamentals with an appreciation for full‑stack software design and is willing to throw themselves into operations research and combinatorial optimisation techniques. You will:
- Extract novel ideas from academic papers (especially deep learning & optimisation)
- Rapidly prototype proofs‑of‑concept (PoCs) and minimal viable products (MVPs)
- Support client and partner projects, from digital transformation and predictive models to agentic workflows, helping organisations accelerate their AI transformation.
This role is ideal for an MSc/MEng graduate (or equivalent) from a top-University with some industrial experience or a demonstrably strong personal portfolio/GitHub.
Key Responsibilities
- Research Translation – Read and critique recent ML/deep‑learning literature; propose how findings can be applied to our platform and client problems.
- Prototype Development – Build PoCs and internal tooling using Python (PyTorch), experiment‑tracking and MLOps best practices.
- Full‑Stack Engineering – Design and implement REST/GraphQL APIs, micro‑services and simple front‑ends (React/Next.js/.NET or similar) to demonstrate solutions end‑to‑end.
- Optimisation & Algorithms – Implement and benchmark optimisation algorithms (discrete/combinatorial, meta‑heuristics, gradient‑based) for deployment at scale (experience in an MILP framework like Gurobi/OR-Tools or equivalent would be ideal but is not required).
- Client & Partner Support – Engage with stakeholders, elicit requirements, present findings, and occasionally embed with partner teams across account management, design and cyber security.
- Collaboration & Knowledge Sharing – Whiteboard ideas, contribute to internal knowledge bases, produce thought-leadership blogs, pair‑program with researchers and full stack engineers.
- Quality & Delivery – Write clean, test‑driven code; set up CI/CD; ensure reproducibility and documentation.
- Continuous Learning – Track emerging trends (LLMs, agentic workflows, RLHF, MLOps) and share insights.
Minimum Qualifications
- MSc (or expected 2025) in Machine Learning, Artificial Intelligence, Computer Science, Data Science, Applied Mathematics, or related STEM background with a strong personal portfolio.
- OR BSc with a proven open‑source/portfolio demonstrating advanced ML projects.
- Working knowledge of deep‑learning frameworks (PyTorch or TensorFlow).
- Experience building and deploying software with Python plus foundational knowledge of one front‑end and one back‑end framework (e.g., FastAPI, Django, Node, React, Vue).
- Solid grasp of data structures, algorithms, and optimisation techniques.
- Familiarity with cloud (AWS/GCP/Azure) services, containers (Docker) and version control (Git).
- Ability to read research papers and translate theory into code.
- Strong communication skills and enthusiasm for white‑boarding problems.
Desirable Extras
- Exposure to large‑language‑model tooling (e.g., LangChain, LlamaIndex, RAG pipelines).
- Experience with agentic workflows, orchestration frameworks, or multi‑agent simulation.
- Understanding and hand-ons experience in graph database design and architecture (e.g. Neo4j).
- Knowledge of MLOps/Kubernetes/Terraform for scalable deployment.
- GPU programming (CUDA) or accelerator libraries (Triton, TVM).
- Academic publications, conference presentations, or hackathon awards.
What We Offer
- Competitive entry level salary with accelerated development opportunities (we have scaled from a headcount of <10 to 25+ in 12 months.)
- Hybrid working: minimum 2–3 days per week on‑site during onboarding; flexible thereafter.
- On-site parking, canteens, world-class gym and sport facilities at Loughborough University.
- Regular company socials.
- Matched pension contributions.
- Access to training, courses and conferences as part of CPD.
- Mentorship from a seasoned research team.
- Opportunity to ship green‑field research into production and shape a platform scheduled for 2026 launch.
Location & Working Pattern
- Advanced Technology Innovation Centre, Loughborough University campus, Leicestershire, UK.
- Hybrid arrangement with on‑site presence at least 50 % of the time during the first 6 - 12 months.
Application Process
The expected application process is as follows:
- Submit CV with a cover letter explaining:
- What excites you about joining a start-up.
- A time that you translated research into code.
- And any links to GitHub/portfolio showcasing ML and full‑stack projects.
- A time you “hacked the system” (outside of academia or your studies)
- (Optional but appreciated) Please send or provide a link to a short video (no more than 2 minutes long) or blog post you have written about a recent paper you find exciting to ben@digitalplanning.tech.
- 20 minute screening call with some of the research team.
- In-person interview with a whiteboard session.
- Small take home technical challenge (will take no more than 1 hour to complete).
- Culture‑fit chat with Directors.
Deadline:Rolling until position filled.
Start Date: Flexible for the right candidate - we can wait out graduate dates etc.
