At a Glance
- Tasks: Lead complex machine learning projects from start to finish, applying scientific thinking.
- Company: Join a multidisciplinary data and AI team focused on real-world solutions.
- Benefits: Competitive salary, flexible working, and opportunities for professional growth.
- Other info: Work in a dynamic environment with a focus on collaboration and innovation.
- Why this job: Make a meaningful impact by solving challenging, real-world problems with ML.
- Qualifications: Proven experience in machine learning and strong Python skills required.
The predicted salary is between 70000 - 90000 ÂŁ per year.
Applying rigorous machine learning to real‑world problems that matter. We're looking for a Senior Machine Learning Engineer to join a multidisciplinary data and AI team delivering high‑impact, real‑world solutions in a secure and highly regulated environment. This is a senior, hands‑on practitioner role, not a people‑management position. You'll operate with a high degree of autonomy, leading complex machine learning work end‑to‑end through technical depth, sound judgement, and delivery credibility. The organisation is outcome‑led rather than technology‑led. Where strong market solutions exist, they are used. Machine learning is built in‑house only where problems are genuinely complex, niche, or sensitive - requiring experimentation, evaluation, and iteration beyond what can be bought. This means the work is thoughtful, challenging, and purposeful, rather than driven by novelty or trend.
What you'll be doing
- Take ownership of complex ML problems, applying scientific thinking and pragmatism in equal measure.
- Lead end‑to‑end machine learning delivery, from problem definition through experimentation, evaluation, and iteration.
- Apply mathematical, statistical, and scientific reasoning to form hypotheses, quantify uncertainty, and interpret results.
- Design and run structured experiments to assess model behaviour, performance, and user impact.
- Work with real, imperfect operational data, not just curated or static datasets.
- Collect, assess, and transform data to support model evaluation and continuous improvement.
- Balance rigour with pragmatism, delivering solutions that are robust, proportionate, and fit for purpose.
- Integrate machine learning components into wider systems, considering performance, reliability, and operational constraints.
- Communicate complex technical ideas clearly to non‑technical stakeholders, enabling informed decision‑making.
- Engage confidently in deep technical design and review discussions with peers.
- Operate effectively within a strong technical assurance and review culture.
- Collaborate with internal teams and selected external partners working at the leading edge of AI.
What we're looking for
- Proven experience operating at senior practitioner level as a Machine Learning Engineer, AI Engineer, Applied ML Scientist, or equivalent.
- Strong grounding in applied mathematics, statistics, and scientific practice.
- Demonstrated ability to evaluate ML models using quantitative evidence and structured experimentation.
- Excellent Python skills for building, evaluating, and iterating on ML solutions.
- Experience working with real‑world, imperfect data from operational systems.
- Strong software engineering practices, including readable, maintainable, and well‑tested code.
- Experience integrating ML components into broader production systems.
- Clear understanding of data ethics, privacy, and responsible use of data.
- Strong communication skills across technical and non‑technical audiences.
- Proven ability to lead work independently and take ownership of outcomes.
The environment evolves, but typical tools include:
- Python for experimentation, modelling, and evaluation.
- Weights & Biases (or equivalent) for experiment tracking.
- AWS, including services such as SageMaker and Bedrock.
- Internally supported AI development platforms and tooling.
This role is not suited to candidates who are dogmatic about specific tools. Adaptability and outcome focus matter more than platform allegiance.
Working in secure, safety‑critical, or highly regulated environments. Background in sectors such as energy, oil & gas, defence, or public sector. Experience within formal technical assurance or governance processes. Collaboration with external suppliers, partners, or research organisations. Comfort operating at pace where quality, safety, and compliance are non‑negotiable.
If you enjoy solving complex problems, applying scientific thinking to messy reality, and delivering ML that stands up to scrutiny, this role offers both challenge and meaning. If interested, apply now!
Senior Machine Learning Engineer in London employer: Lorien
Contact Detail:
Lorien Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Machine Learning Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Showcase your skills! Create a portfolio of your machine learning projects, especially those that demonstrate your ability to tackle real-world problems. This will give you an edge when discussing your experience during interviews.
✨Tip Number 3
Prepare for technical discussions! Brush up on your Python skills and be ready to dive deep into your past projects. Be prepared to explain your thought process and how you approached complex ML challenges.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team.
We think you need these skills to ace Senior Machine Learning Engineer in London
Some tips for your application 🫡
Show Your Passion for Real-World Problems: When writing your application, make sure to highlight your enthusiasm for tackling complex machine learning challenges that have a real impact. We want to see how you've applied your skills to solve genuine problems, so share specific examples!
Be Clear and Concise: We appreciate straightforward communication, especially when it comes to technical concepts. Use clear language to explain your experience and skills, making it easy for us to understand your qualifications without getting lost in jargon.
Demonstrate Your Hands-On Experience: Since this is a hands-on role, it's crucial to showcase your practical experience with machine learning. Talk about the projects you've led, the data you've worked with, and the outcomes you've achieved. We love seeing evidence of your work!
Apply Through Our Website: Don't forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it makes the whole process smoother for everyone involved.
How to prepare for a job interview at Lorien
✨Know Your ML Fundamentals
Brush up on your applied mathematics, statistics, and scientific reasoning. Be ready to discuss how you've used these skills in real-world scenarios, especially when dealing with imperfect data. This will show that you can apply theory to practice, which is crucial for the role.
✨Prepare for Technical Deep Dives
Expect to engage in detailed technical discussions about machine learning models and their performance. Prepare examples of past projects where you led end-to-end delivery, focusing on your problem definition, experimentation, and evaluation processes. This will demonstrate your hands-on experience and technical depth.
✨Communicate Clearly with Non-Technical Stakeholders
Practice explaining complex technical concepts in simple terms. You’ll need to convey your ideas to non-technical audiences, so think of ways to make your explanations relatable. This skill is vital for enabling informed decision-making within the team.
✨Showcase Your Adaptability
Be ready to discuss how you've navigated different tools and platforms in your previous roles. Highlight your ability to focus on outcomes rather than being tied to specific technologies. This aligns with the organisation's approach of using strong market solutions when available.