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- Tasks: Deploy and optimise advanced machine learning models in production environments.
- Company: Join a fast-moving tech company focused on innovation and collaboration.
- Benefits: Competitive salary, flexible working 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 inference systems and strong Python programming skills.
- Other info: Dynamic team environment with a focus on cutting-edge technology.
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 Slough employer: Block MB
Contact Detail:
Block MB Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer in Slough
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups or webinars, 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 gives potential employers a taste of what you can do and sets 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 the attention it deserves.
We think you need these skills to ace Machine Learning Engineer in Slough
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with ML infrastructure and deployment. Use keywords from the job description to show we’re on the same page about what you bring to the table.
Showcase Your Projects: Include specific examples of projects where you've deployed machine learning models. We love seeing how you've tackled challenges, especially in production environments, so don’t hold back!
Be Clear and Concise: When writing your application, keep it straightforward. We appreciate clarity, so make sure your skills and experiences shine through without unnecessary fluff.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity!
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 efficiency and scalability.
✨Demonstrate Your Problem-Solving Skills
Prepare examples of how you've tackled complex technical challenges in previous roles. Highlight your troubleshooting skills, especially in performance optimisation and system reliability. This will show that you can think critically and adapt in a fast-paced environment.
✨Collaborate and Communicate
Since this role involves working closely with research and engineering teams, be ready to discuss your experience in cross-functional collaboration. Share examples of how you’ve effectively communicated technical concepts to non-technical stakeholders, as this is key in a collaborative setting.
