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- Tasks: Build and optimise ML pipelines for large models and scale workloads across distributed systems.
- Company: Venture-backed deep-tech startup at the forefront of machine learning innovation.
- Benefits: Competitive salary, flexible work environment, and opportunities for professional growth.
- Why this job: Join a high-ownership role and make a real impact in cutting-edge ML applications.
- Qualifications: MSc or PhD in relevant fields and strong Python skills required.
- Other info: Fast-paced environment with excellent career advancement opportunities.
The predicted salary is between 28800 - 48000 £ per year.
A venture-backed deep-tech startup is hiring a Machine Learning Engineer with strong experience in scaling training and inference pipelines for modern foundation models. You’ll work at the intersection of ML research, infrastructure, and product engineering - turning cutting-edge model code into scalable, reliable systems used in real-world applications. This is a high-ownership role suited for someone who loves distributed systems, multi-GPU scaling, model optimization, and fast iteration.
What You'll Do
- Build and optimize training & inference pipelines for large models (Transformers, SSMs, Diffusion, etc.)
- Scale workloads across multi-GPU and distributed systems
- Optimize model performance, latency, memory usage, and throughput
- Productionize research code into robust, repeatable systems
- Work closely with researchers to convert exploratory notebooks into production frameworks
- Own ML infrastructure components — data loading, distributed compute, experiment tracking
- Design modular, reusable ML components used across the engineering org
Requirements
- MSc or PhD in Machine Learning, Computer Science, Applied Math, or related field
- Strong Python engineering fundamentals
- Deep experience with PyTorch, JAX, or TensorFlow
- Hands-on experience scaling ML systems in production environments
- Familiarity with MLOps tools (Weights & Biases, Ray, Docker, etc.)
- Experience with modern architectures: Transformers, Diffusion Models, SSMs
- Strong sense of ownership and comfort working in fast-paced early-stage environments
Nice-to-Haves
- Contributions to open-source ML tooling
- Experience with distributed training, model compression, or high-throughput serving
- Experience building or integrating ML systems into end-user applications
- Background in scientific computing, biotech, or computational biology (not required)
Machine Learning Engineer in London employer: Skills Alliance
Contact Detail:
Skills Alliance 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 potential colleagues on LinkedIn. We all know that sometimes it’s not just what you know, but who you know that can help you land that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving ML systems, distributed training, or any cool models you've worked on. We want to see your hands-on experience, so make it easy for recruiters to see what you can do.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python fundamentals and ML concepts. We recommend doing mock interviews with friends or using platforms that focus on coding challenges. The more comfortable you are, the better you'll perform!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who take the initiative to apply directly. Don’t hesitate – get your application in and let’s get the conversation started!
We think you need these skills to ace Machine Learning Engineer in London
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your experience with scaling training and inference pipelines. We want to see your strong Python fundamentals and any hands-on experience you have with PyTorch, JAX, or TensorFlow. Don’t hold back on showcasing your technical prowess!
Tailor Your Application: Take a moment to customise your application for the Machine Learning Engineer role. Mention specific projects where you've optimised model performance or worked with distributed systems. This helps us see how you fit into our fast-paced environment.
Be Clear and Concise: When writing your application, keep it clear and to the point. We appreciate straightforward communication, so avoid jargon unless it’s relevant. Make it easy for us to understand your experience and how it relates to the role.
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 don’t miss out on any important updates. Plus, we love seeing applications come in through our own platform!
How to prepare for a job interview at Skills Alliance
✨Know Your Models
Make sure you’re well-versed in the latest ML models like Transformers and Diffusion Models. Be ready to discuss how you've scaled training and inference pipelines in your previous roles, as this will show your practical experience and understanding of the technology.
✨Showcase Your Python Skills
Since strong Python engineering fundamentals are a must, prepare to demonstrate your coding skills. You might be asked to solve problems on the spot, so brush up on your Python knowledge and be ready to explain your thought process clearly.
✨Familiarise with MLOps Tools
Get comfortable with tools like Weights & Biases, Ray, and Docker. Being able to discuss how you’ve used these tools in past projects will highlight your hands-on experience and readiness to tackle the role's requirements.
✨Emphasise Ownership and Fast Iteration
This role requires a strong sense of ownership and the ability to work in a fast-paced environment. Prepare examples from your past experiences where you took initiative and iterated quickly on projects, showcasing your adaptability and problem-solving skills.
