At a Glance
- Tasks: Push the boundaries of AI design and build impactful systems for engineers.
- Company: Join a pioneering start-up revolutionising generative engineering with a collaborative spirit.
- Benefits: Competitive salary, career growth, and hands-on experience in a dynamic environment.
- Other info: Be part of a team that values continuous improvement and innovation.
- Why this job: Make a real difference in the engineering world while working with cutting-edge technology.
- Qualifications: PhD in Machine Learning or related field, with practical ML/AI production experience.
The predicted salary is between 60000 - 80000 £ per year.
Generative Engineering is bringing AI design into the real world by enabling generative engineering design for physical products. Our focus is creating millions more engineers globally and giving them the data and knowledge necessary to make efficient decisions quickly, one of the main challenges of the physical engineering industry today.
Our team has a background in scaling software to millions of users and successfully disrupting industries, creating Unicorns and Decacorns along the way. We combine the advantages of an early-stage start-up with the ability to focus on creating high-quality, high-impact systems, without the distraction of fundraising.
We are looking for a Machine Learning Engineer to join the team — someone who can operate across the full spectrum from research to production. This role sits closer to the research end: you'll be pushing the frontier on generative models for physical design while also shipping real systems that engineers use every day. Please show both the quality of your past research and any production impact it has had.
Must Haves- PhD in Machine Learning, Computer Science, Applied Mathematics, or a closely related field, with original contributions to deep learning, reinforcement learning, or generative models.
- Formal background in generative modelling — working knowledge of the transformer architecture, diffusion models, flow matching, and variational autoencoders: their evolution, their tradeoffs, and where they're going.
- Real world experience building ML/AI systems that reached production, not just research prototypes.
- Practical experience managing research projects end to end — from problem formulation through to evaluation and deployment.
- Knowledge of modern, larger-scale Python and the ML stack (PyTorch, JAX, or equivalent). You write research-grade code.
- Practical experience building large-scale data pipelines. We don't have data infrastructure — you'll help build it.
- Experience in a high-pace startup environment.
- Knowledgeable about physical engineering or related domains such as robotics or cognitive science.
- Experience working with PINNs (physics-informed neural networks) or graph neural networks for physics-based surrogate models.
- Experience owning or being involved longer-term in an open source project, ideally in a related field such as ML tooling or scientific computing.
- Experience with GPU cluster orchestration.
- Experience with vector embeddings, ideally retrieval-augmented generation (RAG) and multi-modal representations (e.g. CLIP).
- Experience with model fine-tuning.
- Experience with Markov chains or (partially-observable) Markov decision processes.
We aim to improve all our colleagues' abilities and careers by exposing them to the bare bones of a tech start-up whilst giving them the opportunity to support the company in any way. If our people continuously improve, so does our product.
Machine Learning Engineer in Slough employer: Generative Engineering
Generative Engineering is an exceptional employer for Machine Learning Engineers, offering a unique blend of early-stage start-up agility and the stability of a well-established team. Our work culture fosters innovation and collaboration, providing employees with ample opportunities for professional growth while contributing to groundbreaking AI design solutions. Located in a dynamic environment, we empower our engineers to push the boundaries of generative models, ensuring that every team member plays a vital role in shaping the future of physical engineering.
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 people in the industry, attend meetups, and connect 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 generative models or ML systems. This will give potential employers a taste of what you can do and how you think.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice coding challenges and be ready to discuss your past research and its impact in detail.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you're genuinely interested in joining our team at Generative Engineering.
We think you need these skills to ace Machine Learning Engineer in Slough
Some tips for your application 🫡
Show Off Your Research:Make sure to highlight your past research in machine learning, especially any original contributions. We want to see how your work has pushed boundaries and made an impact in production settings.
Be Specific About Your Skills:When listing your skills, be specific about your experience with generative models and the ML stack. Mention tools like PyTorch or JAX, and don’t forget to include your coding prowess — we love research-grade code!
Talk About Real-World Impact:We’re keen on seeing how your projects have transitioned from research to real-world applications. Share examples of systems you’ve built that engineers actually use, and how they’ve improved processes.
Follow Our Application Process:To make things easier for us both, apply through our website. It’s straightforward and ensures your application gets to the right place. And remember, sprinkle 'Salmon' somewhere in your application to show you’ve read the advert!
How to prepare for a job interview at Generative Engineering
✨Showcase Your Research
Make sure to highlight your past research contributions, especially those related to deep learning and generative models. Be prepared to discuss how your work has impacted production systems, as this will demonstrate your ability to bridge the gap between theory and practical application.
✨Know Your Tech Stack
Familiarise yourself with the modern ML stack, particularly Python libraries like PyTorch or JAX. During the interview, be ready to talk about your experience with these tools and how you've used them to build scalable ML systems. This shows you’re not just a researcher but also someone who can deliver real-world solutions.
✨Project Management Experience
Be prepared to discuss your experience managing research projects from start to finish. Highlight specific examples where you formulated problems, evaluated results, and deployed solutions. This will show that you can handle the end-to-end process, which is crucial for the role.
✨Engage with the Company’s Vision
Understand Generative Engineering's mission and how they aim to disrupt the physical engineering industry. Be ready to share your thoughts on how your skills can contribute to their goals. Mentioning 'Salmon' in your application is a fun way to show you’ve read the job advert, so don’t forget that little detail!
