At a Glance
- Tasks: Push the boundaries of AI design and build impactful ML systems for engineers.
- Company: Join a dynamic start-up revolutionising generative engineering with a collaborative spirit.
- Benefits: Competitive salary, career growth, and hands-on experience in a cutting-edge environment.
- Other info: Be part of a team that values continuous improvement and personal development.
- Why this job: Make a real difference in the engineering world while working on innovative AI projects.
- Qualifications: PhD in relevant fields and proven experience in ML/AI systems and research.
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.
Nice to Have
- 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.
Just state the word 'Salmon' anywhere in your application, just to prove you can read a job advert :) 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 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 company. Our collaborative work culture fosters innovation and personal growth, providing employees with opportunities to engage in cutting-edge research while contributing to impactful systems used by engineers globally. Located in a vibrant tech hub, we empower our team to build essential data infrastructure and advance their careers in a supportive environment that values continuous improvement.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer
✨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 past projects, especially those that had real-world impact. This is your chance to demonstrate your expertise in generative models and ML systems.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Be ready to discuss your research and how it translates into practical applications in the engineering space.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are genuinely interested in joining our mission to revolutionise engineering with AI.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Show Off Your Research:Make sure to highlight your past research contributions, especially those related to deep learning and generative models. We want to see how your work has made an impact in production, so don’t hold back on the details!
Be Specific About Your Skills:When listing your skills, be specific about your experience with tools like PyTorch or JAX. We’re looking for someone who can write research-grade code and build large-scale data pipelines, so let us know how you’ve done this in the past.
Connect Your Experience to Our Needs:Tailor your application to show how your background aligns with our focus on generative engineering design. If you have experience in a high-pace startup environment or with physical engineering, make sure to mention it!
Don’t Forget the Salmon!:Just a little reminder to include the word 'Salmon' somewhere in your application. It’s a fun way to show us you’ve read the job advert thoroughly, and we appreciate attention to detail!
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 PyTorch or JAX. Be ready to discuss your experience with building large-scale data pipelines and how you can contribute to creating the data infrastructure they need. This shows you're not just a researcher but someone who can deliver real-world solutions.
✨Understand Generative Modelling
Brush up on your knowledge of generative modelling techniques, including transformers, diffusion models, and variational autoencoders. Be prepared to discuss their trade-offs and future directions, as this will show that you’re not only knowledgeable but also forward-thinking in your approach.
✨Embrace the Start-Up Culture
If you have experience in a high-paced start-up environment, share specific examples of how you've thrived in such settings. Talk about your adaptability and how you can contribute to a dynamic team focused on innovation and efficiency. This will resonate well with their start-up ethos.
