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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 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 Slough employer: Skills Alliance
Contact Detail:
Skills Alliance 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, 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 projects, especially those involving ML systems, distributed training, or any cool models you've worked on. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and ML fundamentals. Practice coding challenges and system design questions related to scaling and optimising ML pipelines. We want you to feel confident when it’s showtime!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Machine Learning Engineer in Slough
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 Python engineering fundamentals shine, so don’t hold back on showcasing your expertise in PyTorch, JAX, or TensorFlow!
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 team!
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 to your experience. Let’s get to the good stuff quickly!
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it makes the whole process smoother for everyone involved.
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, as this will show your hands-on experience and understanding of the technology.
✨Showcase Your Python Skills
Since strong Python fundamentals are a must, prepare to demonstrate your coding skills. You might be asked to solve a problem on the spot, so brush up on your Python knowledge and be ready to explain your thought process clearly.
✨Familiarise Yourself with MLOps Tools
Get comfortable with tools like Weights & Biases, Ray, and Docker. Being able to discuss how you’ve used these tools in production environments will set you apart and show that you can handle the infrastructure side of things.
✨Emphasise Your Ownership Mindset
This role requires a strong sense of ownership, so be prepared to share examples of how you've taken initiative in past projects. Highlight situations where you’ve turned exploratory work into robust systems, showcasing your ability to thrive in fast-paced environments.
