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Cosine is a UK AI lab building frontier machine learning models, applications and training infrastructure for mission-critical defence and national security organisations. They are developing coding agents, specialised large language models (Lumen), AI-native security products and deployment infrastructure for advanced AI in demanding environments.
You will join as Applied ML Technical Lead, reporting directly to the CEO. This is a hands-on technical leadership role combining machine learning development, deployment and customer-facing technical delivery. You will own the technical direction and day-to-day progress of applied ML work for Defence programmes, coordinating researchers, engineers and stakeholders to translate promising research into operational capability.
Key responsibilities include:
- Design, build, train and evaluate machine learning models for Defence and national security use cases involving complex sensor data, signal processing, pattern recognition, classification and multimodal intelligence.
- Develop model architectures, training pipelines, evaluation frameworks and inference systems; work with real-world and synthetic datasets to improve accuracy, robustness and generalisation.
- Translate research prototypes into reliable, deployable capabilities operating within demanding technical and security constraints.
- Own technical direction and day-to-day progress of applied ML projects; turn operational objectives into concrete technical goals, experiments and milestones.
- Coordinate work across ML researchers, software engineers, infrastructure teams and external stakeholders; identify dependencies, risks and blockers.
- Engage directly with Defence and national security stakeholders to understand operational challenges, translate requirements into technical objectives and demonstrate progress.
- Deploy ML models into real-world environments including secure, on-premise and resource-constrained systems; optimise for inference performance, integration and reliability.
- Establish feedback loops between operational users and engineering teams to ensure deployed systems are genuinely useful.
You will not act as a traditional project manager but rather as a technical owner balancing hands-on engineering with coordination and planning.
In your first three months, you will become the technical lead for a major applied Defence ML programme, build relationships with researchers and customer stakeholders, develop understanding of existing models and architecture, establish a focused delivery plan with clear priorities and milestones, make hands-on contributions to model development or deployment, and demonstrate tangible progress to customers.
REQUIREMENTS:
Must-have:
- Background in applied machine learning, ML engineering, research engineering or similarly technical role with experience taking ownership of complex technical work.
- Production-quality Python and modern ML frameworks (PyTorch).
- Experience building, training, fine-tuning and evaluating machine learning models.
- Working with complex datasets, designing experiments and interpreting model performance.
- Understanding trade-offs between model performance, compute requirements, latency and deployment constraints.
- Engineering infrastructure experience: Git, containers, Linux, GPU compute.
- Technical ownership of projects with multiple stakeholders, uncertain requirements and evolving priorities.
- Clear communication of technical concepts to researchers, engineers, senior stakeholders and operational users.
- Ability to move between model development, customer discussions and delivery planning without losing technical depth.
- Sound technical judgement, effective prioritisation and practical problem-solving.
- Eligibility to hold UK Security Vetting Clearance SC or above (no prior clearance required).
Nice-to-have:
- Existing UK security clearance (SC or DV) or prior experience in secure Defence environments.
- Signal processing, time-series data, sensor fusion, representation learning or ML applied to complex real-world datasets.
- Foundation models, multimodal learning or advanced model training and post-training techniques.
- Deploying ML models to edge devices or constrained compute environments.
- Familiarity with UK Defence procurement, technical assurance or national security landscape.
- Experience leading technical workstreams or as technical lead on customer-facing ML projects.
