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- Tasks: Deploy and optimise machine learning models for high-performance systems.
- Company: Join a dynamic tech company focused on innovation and collaboration.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Why this job: Tackle complex challenges and make a real impact in the ML field.
- Qualifications: Experience with ML systems, Python programming, and cloud technologies.
- Other info: Fast-paced environment with strong team collaboration and career advancement.
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
- 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
- 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 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 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’ve got some fantastic opportunities waiting for you, and applying directly can sometimes give you an edge. Plus, it’s super easy to keep track of your applications!
We think you need these skills to ace Machine Learning Engineer in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with ML infrastructure and deployment. We want to see how you've tackled similar challenges in the past, so don’t hold back on those details!
Showcase Your Skills: When writing your application, emphasise your programming skills in Python and any experience with containerisation tools like Kubernetes. We love seeing candidates who can hit the ground running!
Be Specific About Your Experience: Don’t just list your previous roles; explain what you did in each one. We’re interested in how you’ve improved performance and reliability in ML systems, so give us the juicy bits!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to keep track of your application and ensure it gets the attention it deserves!
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.
✨Showcase Your Technical Skills
Brush up on your Python programming skills and be ready to demonstrate your familiarity with modern ML frameworks. If you've worked with containerisation tools like Kubernetes, have examples ready to share. This will highlight your technical prowess and readiness for the role.
✨Prepare for Problem-Solving Questions
Expect questions that test your troubleshooting skills, especially around performance and scaling issues. Think of specific scenarios where you identified a problem and how you resolved it. This will illustrate your analytical thinking and ability to work under pressure.
✨Emphasise Collaboration Experience
Since this role involves working closely with research and engineering teams, be ready to discuss your experience in cross-functional environments. Share examples of how you’ve collaborated on projects, improved deployment processes, or contributed to infrastructure improvements. This will show you’re a team player who thrives in a collaborative setting.
