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- Tasks: Support deployment and optimisation of advanced machine learning models in production environments.
- Company: Join a forward-thinking tech company focused on innovation and collaboration.
- Benefits: Enjoy hybrid remote work, competitive salary, and opportunities for professional growth.
- Why this job: Make an impact by working at the cutting edge of machine learning technology.
- Qualifications: Experience with ML inference systems, strong Python skills, and troubleshooting expertise.
- Other info: Dynamic role with opportunities to collaborate across research and engineering teams.
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.
- Productionise machine learning models from research through validation, staging and live deployment
- 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
Proven experience deploying and maintaining ML inference systems in production environments. Strong programming experience in Python and familiarity with modern machine learning frameworks. Experience supporting GPU workloads and performance optimisation. Strong troubleshooting skills across performance, scaling and system reliability. 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 - Hybrid Remote 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 - Hybrid Remote in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We can’t stress enough how important it is to make connections; you never know who might have the inside scoop on job openings.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects. We recommend including any models you've deployed or optimised, as this will give potential employers a taste of what you can do.
✨Tip Number 3
Prepare for those interviews! Brush up on your technical knowledge and be ready to discuss your experience with ML infrastructure. We suggest practising common interview questions and even doing mock interviews with friends.
✨Tip Number 4
Apply through our website! We’ve got loads of opportunities waiting for you. By applying directly, you’ll ensure your application gets the attention it deserves, and we’re here to support you every step of the way.
We think you need these skills to ace Machine Learning Engineer - Hybrid Remote in 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 infrastructure, deployment processes, and any relevant projects that showcase your skills in Python and modern ML frameworks.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about ML and how your background aligns with the job description. Don’t forget to mention specific experiences that demonstrate your ability to improve deployment efficiency and scalability.
Showcase Your Projects: If you've worked on any relevant projects, make sure to include them in your application. Whether it's deploying models or optimising GPU workloads, real examples can really set you apart from other candidates.
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of your application and ensures you don’t miss out on any important updates!
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 transitioned them from research to production, including any challenges you faced and how you overcame them.
✨Show Off Your Programming Skills
Brush up on your Python skills and be ready to demonstrate your familiarity with modern machine learning frameworks. You might be asked to solve a coding problem or discuss your approach to optimising GPU workloads.
✨Understand Deployment Workflows
Familiarise yourself with model serving and deployment workflows. Be ready to talk about how you've improved deployment processes in the past and any tools you've used for monitoring system performance.
✨Collaborate Like a Pro
This role involves working closely with various teams, so be prepared to discuss your experience collaborating with research, engineering, and infrastructure teams. Highlight any successful projects where teamwork led to improved deployment efficiency.
