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For Companies At a Glance
- Tasks: Build and optimise ML pipelines for cutting-edge models in a fast-paced startup.
- 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 on transformative ML applications.
- Qualifications: MSc or PhD in relevant fields and strong Python skills required.
- Other info: Dynamic team with a focus on innovation and collaboration.
The predicted salary is between 36000 - 60000 £ 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 City of 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 City of London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with fellow ML enthusiasts. 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 involving scaling training and inference pipelines. This will give potential employers a taste of what you can do and how you approach real-world problems.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python fundamentals and ML concepts. Practice coding challenges and system design questions that focus on distributed systems and model optimization to impress your interviewers.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Tailor your application to highlight your experience with PyTorch, TensorFlow, and any MLOps tools you've used.
We think you need these skills to ace Machine Learning Engineer in City of 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 necessary. 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 Inside Out
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, and have specific examples at hand to showcase your expertise.
✨Showcase Your Python Skills
Since strong Python fundamentals are a must, brush up on your coding skills before the interview. Prepare to solve coding challenges or explain your thought process while working with libraries like PyTorch, JAX, or TensorFlow.
✨Familiarise Yourself with MLOps Tools
Get comfortable with tools like Weights & Biases, Ray, and Docker. Be prepared to discuss how you’ve used these tools in past projects to optimise model performance and streamline workflows.
✨Demonstrate Ownership and Initiative
This role requires a strong sense of ownership, so be ready to share instances where you took the lead on projects. Highlight your ability to work in fast-paced environments and how you’ve successfully managed multiple tasks or challenges.
