Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Deploy and optimise machine learning models for high-performance systems.
- Company: Join a cutting-edge tech firm focused on ML innovation.
- Benefits: Competitive salary, flexible work options, and growth opportunities.
- Why this job: Tackle complex challenges and make a real impact in ML deployment.
- Qualifications: Experience with ML systems, Python programming, and cloud infrastructure.
- Other info: Collaborative environment with a focus on continuous learning.
The predicted salary is between 36000 - 60000 £ per year.
My client is looking for an experienced ML Infrastructure Engineer to support the deployment, optimisation and scaling of advanced machine learning models in production environments. This role sits at the intersection of research and engineering, focused on ensuring models are reliably transitioned from experimentation through to large-scale deployment.
You will work closely with research and platform teams to build and maintain high-performance inference systems, improve deployment processes and help drive infrastructure improvements that enable faster model iteration and release cycles. This is a strong opportunity to work on technically complex challenges within a fast-moving and highly collaborative environment.
The Role
- Productionise machine learning models from research through validation, staging and live deployment
- Build, maintain and optimise scalable inference infrastructure supporting high-throughput, low-latency workloads
- Improve performance and reliability across GPU-based environments
- Design and implement model serving and deployment workflows
- Develop monitoring and observability tools to track system performance, errors and utilisation
- Support data preparation and model integration as part of the wider development lifecycle
- Collaborate with research, engineering and infrastructure teams to improve deployment efficiency and platform scalability
- Evaluate and integrate third-party infrastructure and inference tooling where appropriate
Requirements
- Proven experience deploying and maintaining ML inference systems in production environments
- Strong programming experience in Python and familiarity with modern machine learning frameworks
- Experience working with containerisation and orchestration technologies such as Kubernetes or similar
- Exposure to distributed systems and cloud-based infrastructure
- Experience supporting GPU workloads and performance optimisation
- Strong troubleshooting skills across performance, scaling and system reliability
- Comfortable working cross-functionally within research-led environments
- Ability to operate in fast-paced teams with evolving technical priorities
Nice to Have
- Experience building or improving model serving infrastructure
- Understanding of distributed training or inference techniques
- Experience debugging low-level performance or hardware-related issues
- Exposure to real-time or latency-sensitive ML applications
Machine Learning Engineer in City of London employer: Block MB
Contact Detail:
Block MB Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer in City of London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those involving deployment and optimisation. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python skills and familiarising yourself with ML frameworks. Practice coding challenges and system design questions that focus on inference systems and deployment workflows.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Plus, it’s a great way to ensure your application gets seen by the right people.
We think you need these skills to ace Machine Learning Engineer in City of London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with ML inference systems, Python programming, and any relevant technologies like Kubernetes. We want to see how your skills match what we're looking for!
Showcase Your Projects: Include specific projects where you've deployed machine learning models or improved infrastructure. We love seeing real examples of your work, so don’t hold back! This helps us understand your hands-on experience.
Be Clear and Concise: When writing your application, keep it clear and to the point. Use bullet points for easy reading and make sure to highlight your key achievements. We appreciate straightforward communication!
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. We can’t wait to see what you bring to the table!
How to prepare for a job interview at Block MB
✨Know Your ML Models Inside Out
Make sure you can discuss the machine learning models you've worked with in detail. Be prepared to explain how you productionised them, the challenges you faced, and how you optimised their performance. This shows your hands-on experience and understanding of the entire lifecycle.
✨Brush Up on Your Tech Stack
Familiarise yourself with the specific technologies mentioned in the job description, like Python, Kubernetes, and GPU workloads. If you have experience with any third-party tools or frameworks, be ready to discuss how they can enhance deployment processes. This will demonstrate your technical fit for the role.
✨Prepare for Problem-Solving Questions
Expect to tackle some technical challenges during the interview. Practice explaining your thought process when troubleshooting performance issues or scaling problems. Use examples from your past experiences to illustrate how you approached these situations and what solutions you implemented.
✨Show Your Collaborative Spirit
Since this role involves working closely with research and engineering teams, be ready to share examples of how you've successfully collaborated in the past. Highlight your ability to communicate effectively across different functions and how you contributed to improving deployment efficiency or platform scalability.
