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- Tasks: Lead the design and delivery of cutting-edge ML systems for real-time predictions.
- Company: Join a mission-driven company focused on simplifying financial decision-making for everyone.
- Benefits: Enjoy hybrid working, generous holidays, private healthcare, and more perks tailored to you.
- Why this job: Be part of an inclusive culture that celebrates creativity and innovation in AI engineering.
- Qualifications: Extensive experience in ML systems, strong Python skills, and a collaborative mindset required.
- Other info: Opportunity to influence ML tooling and work with talented teams in a supportive environment.
The predicted salary is between 48000 - 84000 ÂŁ per year.
Staff Machine Learning Engineer at Compare the MarketJoin Compare the Market and help to make financial decisionâmaking a breeze for millions.
Job DescriptionFunction: Data
Location: Hybrid, London office
We\âre a purposeâdriven business powered by tech and AI. We\âre building highâperforming, resultsâdriven teams with the skills, mindset, and ambition to deliver outcomes at pace. Every role here plays a part in driving our mission forward, and we create an environment where you can bring your authentic self, grow a truly characterful career, and see the direct impact of your work on the lives of our customers.
We\âve carved a meerkatâshaped niche and we\âre looking for ambitious, curious thinkers who thrive in a fastâmoving, highâimpact environment. If you love accountability, embrace challenge, and want to make a real difference, you\âll fit right in.
As a Staff Machine Learning Engineer, you\âll play a pivotal role in designing, scaling, and evolving the machine learning infrastructure that powers Compare the Market\âs most ambitious AI products. From LLMâbased personalisation to realâtime optimisation systems, you\âll help define how models are developed, deployed, and maintained in productionâreliably and responsibly. This is a highâimpact, handsâon leadership role. You\âll work across product, data science, and engineering to lead delivery of complex ML systems. You\âll also define the core MLOps capabilities for the business and establish the standards and patterns that accelerate safe, scalable AI deployment across teams.
ML Systems Design & Delivery
Lead the architecture and delivery of ML systems that power realâtime and batch predictions at scale.
Design production pipelines for training, deployment, and monitoring using modern MLOps tooling.
Take ownership of technical quality, resilience, and observability of critical ML services.
Build reusable tools and frameworks to enable fast, safe experimentation and deployment.
Platform, Standards & MLOps Foundations
Define and build the core MLOps capabilities for the organisation, including training pipelines, deployment frameworks, and observability tooling.
Establish standardised patterns and best practices to accelerate model development, testing, and deployment.
Lead the evolution of our ML platform, working with engineering partners to improve scalability, governance, and developer experience.
Contribute to responsible ML practicesâsupporting auditability, explainability, and model health monitoring.
Technical Leadership & Collaboration
Partner with data scientists to take models from prototype to production with clear interfaces and robust engineering.
Lead crossâteam technical design sessions and architectural reviews.
Provide mentorship, pair programming, and code reviews for other engineers across the AI function.
Innovation & Culture
Stay ahead of developments in MLOps, LLM infrastructure, and AI engineering best practices.
Influence longâterm strategic direction for ML tooling and delivery across the organisation.
Help build a highâperforming, inclusive, and collaborative ML Engineering culture.
Qualifications
Extensive experience designing and deploying ML systems in production.
Deep technical expertise in Python and modern ML tooling (MLflow, TFX, Airflow, Kubeflow, SageMaker, Vertex AI).
Experience with infrastructureâasâcode and CI/CD practices for ML (Terraform, GitHub Actions, ArgoCD).
Proven ability to build reusable tooling, scalable services, and resilient pipelines for realâtime and batch inference.
Strong understanding of ML system lifecycle: testing, monitoring, governance, observability.
Excellent collaboration and communication skills; able to influence crossâfunctional teams and lead complex technical work.
A background in software engineering, computer science, or a quantitative fieldâor equivalent experience leading ML systems in production.
Why Compare the Market?We\âre a business built for pace and performance. Here, you\âll be encouraged to think differently, act boldly, and deliver brilliantly in a culture that values results and rewards progress.
We believe diverse teams make better decisions, and we\âre committed to creating an inclusive workplace where everyone feels empowered to grow, contribute, and thrive.
If you\âre ready to stretch yourself, raise the bar, and grow with a team that\âs serious about performance, innovation, and purpose, we\âd love to hear from you.
Seniority level
MidâSenior level
Employment type
Fullâtime
Job function
Engineering and Information Technology
Industries
Software Development
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Staff Machine Learning Engineer employer: Compare the Market
Contact Detail:
Compare the Market Recruiting Team
StudySmarter Expert Advice đ¤Ť
We think this is how you could land Staff Machine Learning Engineer
â¨Tip Number 1
Familiarise yourself with the latest MLOps tools and practices mentioned in the job description, such as MLflow, TFX, and Kubeflow. Being able to discuss these technologies confidently during your interview will demonstrate your readiness for the role.
â¨Tip Number 2
Showcase your experience with Python and CI/CD practices by preparing examples of past projects where you successfully implemented these skills. This will help you illustrate your technical expertise and problem-solving abilities.
â¨Tip Number 3
Prepare to discuss how you've collaborated with cross-functional teams in previous roles. Highlight specific instances where your communication skills led to successful project outcomes, as this is crucial for the collaborative culture at StudySmarter.
â¨Tip Number 4
Stay updated on the latest trends in AI and machine learning, particularly around LLM infrastructure. Being knowledgeable about current developments will not only impress your interviewers but also show your passion for the field.
We think you need these skills to ace Staff Machine Learning Engineer
Some tips for your application đŤĄ
Tailor Your CV: Make sure your CV highlights your extensive experience in designing and deploying ML systems. Emphasise your technical expertise in Python and modern ML tooling, as well as any relevant projects that showcase your skills.
Craft a Compelling Cover Letter: In your cover letter, express your passion for machine learning and how it aligns with the company's mission. Mention specific examples of your previous work that relate to the responsibilities outlined in the job description, such as leading architectural design or building reusable tools.
Showcase Collaboration Skills: Highlight your collaboration and communication skills in your application. Provide examples of how you've influenced cross-functional teams or led complex technical projects, as these are key aspects of the role.
Demonstrate Continuous Learning: Mention any recent courses, certifications, or self-directed learning you've undertaken in MLOps, AI engineering best practices, or related fields. This shows your commitment to staying ahead in the rapidly evolving tech landscape.
How to prepare for a job interview at Compare the Market
â¨Showcase Your ML Expertise
Be prepared to discuss your extensive experience in designing and deploying machine learning systems. Highlight specific projects where you've successfully implemented ML solutions, focusing on the tools and frameworks you used.
â¨Demonstrate Technical Leadership
Since this role involves leading cross-team technical design sessions, be ready to share examples of how you've influenced technical decisions in previous roles. Discuss your approach to mentoring and collaborating with data scientists and engineers.
â¨Understand MLOps Practices
Familiarise yourself with modern MLOps tooling and practices, such as CI/CD for ML. Be prepared to explain how you've implemented these in past projects, particularly focusing on infrastructure-as-code and deployment frameworks.
â¨Emphasise Collaboration Skills
This role requires excellent collaboration and communication skills. Think of examples where you've successfully worked with cross-functional teams to deliver complex ML systems, and be ready to discuss how you can influence and lead within a team.
