Machine Learning Engineer
Role details
Job location
Tech stack
Job description
We are growing our ML team to support new projects and product developments. We are looking for AI builders, you will be working on developing and deploying AI systems to solve complex problems that have real-world impact. You'll join an existing ML team that works in close collaboration with software, hardware and systems teams to get useful AI into the hands of users. Our ML team works end-to-end-from R&D to deployment-across traditional ML, deep learning, data engineering and LLM/agentic systems. As a ML Engineer you will be contribute to projects and products, from applied research to delivering ML in production on edge deployments. You will commit to continual learning and developing your craft. You will be expected to maintain coding standards and follow ML, data, and software best practices. No prior defence experience is required. We're interested in people who are passionate about getting AI systems into the hands of end users, that deliver tangible value, whatever the sector. You should be curious, with a desire to learn, develop and stay at the cutting edge., * Build and ship: contribute to models and services from prototyping to production; write maintainable code, tests, and docs.
- Experimentation: collect and curate data, engineer features, train and evaluate models, and iterate with measurable outcomes.
- MLOps in practice: build and support training/serving pipelines, experiment tracking, CI/CD for ML, and basic observability.
- Collaborate widely: work with software, systems, and product colleagues to deliver features effectively.
- Share knowledge: pair with teammates, participate in code reviews, and contribute to a positive, pragmatic engineering culture.
Requirements
Do you have experience in Unity?, Do you have a Master's degree?, * Applied ML experience: typically 1-5 years developing and delivering ML systems.
- ML fundamentals: solid grounding in core ML/DL methods and the maths that makes them work; you can reason about failure modes and trade-offs.
- LLMs & agentic systems: some hands-on experience (e.g., RAG, evaluation, prompt tooling) and eagerness to deepen expertise.
- MLOps foundations: containerisation, reproducible training, experiment tracking, model packaging/serving, basic observability.
- Data engineering: experience with Databricks and its toolchain-Apache Spark, Delta Lake, MLflow, Unity Catalog, Databricks SQL, and Databricks Workflows.
- Software development: Strong python skills, experience with low-level languages like Rust is desirable.
- Product mindset & communication: you care about user outcomes and can explain decisions clearly to non-ML teammates.
- Builder, not just theorist: you like turning ideas into running systems and iterating with feedback.
Beneficial knowledge
- General tooling and platforms: Databricks, AWS, GitHub, Docker/Kubernetes, MLflow, Jira.
- Edge deployments: Nvidia Jetson (e.g. AGX Orin), Raspberry Pi, or other embedded accelerators.
- LLM/Agent tooling: DSPy, llama.cpp, vLLM, evaluation harnesses, prompt optimisation, agent frameworks.