At a Glance
- Tasks: Design and develop cutting-edge machine learning models for national security.
- Company: BAE Systems Digital Intelligence, a global leader in cyber and intelligence.
- Benefits: Flexible work hours, 25 days holiday, private medical insurance, and career development support.
- Why this job: Make a real impact on national security while working with innovative technologies.
- Qualifications: Experience in ML model development, AWS services, and strong problem-solving skills.
- Other info: Join a resourceful team dedicated to delivering high-quality solutions across the public sector.
The predicted salary is between 48000 - 72000 ÂŁ per year.
BAE Systems Digital Intelligence is a global cyber and intelligence firm with 4,500 experts across 10 countries. We collect, connect and understand complex data to provide digital advantage in the most demanding environments for governments, armed forces and commercial businesses.
We are hiring a Senior ML Engineer to design, develop and iterate machine learning models that underpin national security objectives. The role will involve collaboration with Data Scientists, Software Engineers, Product Management and Government stakeholders throughout the full lifeâcycle from hypothesis through to production deployment, leveraging AWSâbased infrastructure and modern MLOps/LLMOps tooling.
Core Duties- Design and develop machine learning models for traditional ML use cases and GenAI/LLM applications.
- Lead experimentation cycles: define hypotheses, design experiments, evaluate results, and iterate rapidly while adhering to governance requirements.
- Transition validated experiments into productionâready solutions, working closely with engineers on deployment and monitoring.
- Build and optimise ML pipelines using AWS services and experiment tracking tools.
- Develop and integrate LLMâpowered solutions for evaluation and production monitoring.
- Implement robust experiment tracking, model versioning and reproducibility practices with full audit trails.
- Design feature engineering approaches and contribute to feature store development.
- Support production models through monitoring, performance analysis and continuous improvement.
- Apply responsible AI practices, including model explainability and fairness assessment.
- Present experiment findings and production outcomes to stakeholders, articulating operational and strategic value.
- Mentor junior colleagues and share learnings across the team.
- Handsâon experience developing and deploying ML models in Python (scikitâlearn, XGBoost, PyTorch, or TensorFlow).
- Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments.
- Proven skill in experiment design: hypothesis formulation, A/B testing methodology and statistical evaluation.
- Track record transitioning models from experimentation to production with appropriate governance and quality controls.
- Experience with experiment tracking and MLOps tooling (MLflow, Weights & Biases, Data Version Control).
- Experience developing LLM/GenAI applications, including prompt engineering and RAG architectures.
- Familiarity with LLMOps tooling such as LangSmith, LangChain or LangGraph.
- Understanding of model evaluation, validation techniques and production monitoring.
- Experience working in crossâfunctional teams from problem framing through to production delivery.
- Ability to communicate complex findings to nonâtechnical audiences clearly.
- Strong problemâsolving skills and the judgment to recognise when AI is not the answer.
- Advanced LLM techniques: agents, tool use, and agentic workflows.
- Vector databases (Pinecone, Weaviate, pgvector) for RAG.
- Feature stores (Feast, AWS Feature Store).
- InfrastructureâasâCode (Terraform, CloudFormation).
- Largeâscale data processing frameworks (Spark, Dask).
- Data governance and compliance frameworks.
- Experience in regulated industries (finance, healthcare, or similar).
Security Clearance: Required. Candidates must be eligible for, or already have, security clearance and willing to go through the required process.
How we will support you- Workâlife balance importance; coreâhour flexibility and partâtime options available.
- Minimum 3 days per week in the office to support client engagement.
- 25 days holiday per year, with option to buy/sell and carry over.
- Private medical and dental insurance; competitive pension; cycleâtoâwork; taste cards and more.
- Dedicated Career Manager to support career development.
- Company bonus scheme participation.
- Access to diversity and support groups across gender, mental health, etc.
Our team is resourceful, innovative and dedicated. We work from a mix of disciplines, delivering highâquality solutions across the public sector. Joining our National Security business means contributing to the most trusted partner for our national security clients, with a legacy of over 40 years of experience.
Senior Machine Learning Engineer in London employer: BAE Systems Digital Intelligence
Contact Detail:
BAE Systems Digital Intelligence Recruiting Team
StudySmarter Expert Advice đ€«
We think this is how you could land Senior Machine Learning Engineer in London
âšTip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. A friendly chat can lead to opportunities that arenât even advertised yet.
âšTip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects. This is your chance to demonstrate what you can do beyond just a CV.
âšTip Number 3
Prepare for interviews by practising common ML questions and scenarios. We recommend doing mock interviews with friends or using online platforms to get comfortable.
âšTip Number 4
Donât forget to apply through our website! Itâs the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive!
We think you need these skills to ace Senior Machine Learning Engineer in London
Some tips for your application đ«Ą
Tailor Your CV: Make sure your CV is tailored to the Senior Machine Learning Engineer role. Highlight your experience with ML models, AWS services, and any relevant projects that showcase your skills. We want to see how you fit into our team!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about machine learning and how your background aligns with our mission at BAE Systems. Let us know what excites you about this role!
Showcase Your Projects: If you've worked on interesting ML projects, don't hold back! Include links to your GitHub or any relevant portfolios. We love seeing practical applications of your skills and how you approach problem-solving.
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you donât miss out on any important updates from us!
How to prepare for a job interview at BAE Systems Digital Intelligence
âšKnow Your ML Models Inside Out
Make sure you can discuss your experience with various machine learning models in detail. Be prepared to explain how you've designed, developed, and deployed models, especially in Python using libraries like scikit-learn or TensorFlow. Highlight specific projects where you transitioned models from experimentation to production.
âšFamiliarise Yourself with AWS Services
Since the role involves working with AWS ML services, brush up on your knowledge of SageMaker, Lambda, and S3. Be ready to discuss how you've used these tools in past projects, particularly in building and optimising ML pipelines. Showing familiarity with MLOps tooling will also give you an edge.
âšPrepare for Experimentation Discussions
Expect questions about your approach to experiment design and hypothesis formulation. Be ready to share examples of A/B testing you've conducted and how you evaluated results. Discussing your experience with experiment tracking tools like MLflow or Weights & Biases will demonstrate your hands-on expertise.
âšCommunicate Complex Ideas Simply
You'll need to present findings to stakeholders who may not have a technical background. Practice explaining complex concepts in simple terms. Think of ways to articulate the operational and strategic value of your work, as this will be crucial in showcasing your ability to bridge the gap between technical and non-technical audiences.