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For Companies At a Glance
- Tasks: Design and deploy scalable ML infrastructure for cutting-edge AI projects.
- Company: Join a pioneering AI startup redefining 3D content generation.
- Benefits: Competitive salary, flexible pay for strong candidates, and a dynamic work environment.
- Why this job: Shape the future of AI and work on groundbreaking technology.
- Qualifications: Experience in building ML infrastructure and cloud architecture from scratch.
- Other info: Be part of a small, fast-moving team with significant growth potential.
The predicted salary is between 72000 - 104000 £ per year.
Job Description
ML & Cloud Infrastructure Engineer
London
Up to £150000
About the Role
A cutting-edge AI start-up is pioneering the development of frontier 3D foundation models, pushing the boundaries of computer vision and spatial computing. Their mission is to redefine how industries such as robotics, AR/VR, gaming, and film generate and interact with 3D content.
They are now seeking an ML & Cloud Infrastructure Engineer to join their growing team.
This is a unique opportunity to work at the forefront of AI innovation, building the infrastructure that underpins complex ML workloads and production systems. You’ll play a central role in scaling the company’s platforms and ensuring their pioneering technology reaches its full potential.
Key Responsibilities,
- Develop and maintain scalable, high-performance cloud-based infrastructure for ML workloads and API deployment.
- Manage and optimize cloud platforms (AWS, Azure, GCP) and set up ML nodes for local and distributed training.
- Install, configure, and monitor servers, ensuring system reliability.
- Design and optimize storage solutions for large-scale ML datasets.
- Manage containerized applications with Docker, Kubernetes, Terraform, and related tools.
- Collaborate with ML engineers and researchers to ensure seamless orchestration of training and production environments.
- Troubleshoot and respond to cloud/production incidents, implementing long-term solutions.
What We’re Looking For
- At least 3 years of professional experience in a cloud-related engineering role (ML-related experience highly desirable).
- Strong scripting skills (Bash, PowerShell, Python, etc.) for automation.
- Proven expertise in at least one major cloud platform (AWS, GCP, or Azure).
- Experience with containerization and orchestration (Docker, Kubernetes).
- Ability to manage and optimize large-scale cloud infrastructure.
- Familiarity with Python (Jupyter) and ML frameworks (e.g., PyTorch).
- Experience with cloud monitoring tools (Prometheus, Grafana).
- Exposure to cloud-based databases (RDS, Aurora, Spanner, etc.) and data-visualisation tools.
- Knowledge of CI/CD tools (e.g., CircleCI).
Machine Learning Infrastructure Engineer employer: Harnham
Contact Detail:
Harnham Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Infrastructure Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues 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 projects, especially those related to ML infrastructure. This is your chance to demonstrate your expertise in building cloud systems from scratch, so make it shine!
✨Tip Number 3
Prepare for technical interviews by brushing up on relevant technologies like Docker, Kubernetes, and cloud platforms. Practice coding challenges and system design questions to show you can handle the demands of the role.
✨Tip Number 4
Apply through our website! We love seeing candidates who take the initiative. Tailor your application to highlight your experience with scalable cloud infrastructure and ML systems, and let us know why you're excited about shaping the future of AI at SpAItial.
We think you need these skills to ace Machine Learning Infrastructure Engineer
Some tips for your application 🫡
Show Your Passion for AI: When writing your application, let your enthusiasm for AI and machine learning shine through. We want to see that you’re not just looking for a job, but that you’re genuinely excited about shaping the future of 3D generative AI with us.
Highlight Relevant Experience: Make sure to detail your experience in building cloud and ML systems from scratch. We’re keen on seeing specific examples of your work with GPU clusters, containerised workflows, and any other relevant tech you've tackled. This is your chance to show us what you can bring to the table!
Tailor Your Application: Don’t just send a generic application! Tailor your CV and cover letter to reflect the key responsibilities and skills mentioned in our job description. We love it when candidates take the time to connect their experiences directly to what we’re looking for.
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of applications better and ensures you don’t miss out on any important updates. Plus, it’s super easy to do!
How to prepare for a job interview at Harnham
✨Know Your Tech Stack
Make sure you’re well-versed in the tech stack mentioned in the job description. Brush up on AWS, GCP, Azure, Kubernetes, Docker, and Terraform. Be ready to discuss your hands-on experience with these tools and how you've used them to build ML infrastructure from scratch.
✨Showcase Your Problem-Solving Skills
Prepare to share specific examples of challenges you've faced while building cloud and ML systems. Highlight your thought process and the steps you took to overcome obstacles. This will demonstrate your ability to think critically and adapt in a fast-paced environment.
✨Understand the Company’s Vision
Research SpAItial and its mission to redefine 3D content generation. Familiarise yourself with their projects and be ready to discuss how your skills can contribute to their goals. Showing genuine interest in their work can set you apart from other candidates.
✨Prepare for Technical Questions
Expect technical questions that assess your knowledge of scalable cloud infrastructure and ML workloads. Practice explaining complex concepts clearly and concisely. You might also want to prepare for practical tests or scenarios that require you to design a system on the spot.