At a Glance
- Tasks: Push the boundaries of AI design and build impactful systems for engineers.
- Company: Join a dynamic start-up revolutionising generative engineering with a collaborative spirit.
- Benefits: Hybrid remote work, competitive salary, and opportunities for personal and professional growth.
- Other info: Work in a fast-paced environment with excellent career development opportunities.
- Why this job: Be at the forefront of AI innovation and make a real difference in engineering.
- Qualifications: PhD in relevant fields and experience in building production-level ML systems.
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.
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 - Hybrid Remote in London employer: Generative Engineering
Generative Engineering is an exceptional employer that fosters a dynamic work culture where innovation thrives. With a focus on employee growth, we provide opportunities to engage in cutting-edge research and production, allowing you to make a tangible impact in the AI design space. Our hybrid remote model ensures flexibility while being part of a passionate team dedicated to transforming the engineering landscape.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer - Hybrid Remote in London
✨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 our field.
✨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 design.
We think you need these skills to ace Machine Learning Engineer - Hybrid Remote in London
Some tips for your application 🫡
Show Off Your Research:Make sure to highlight your past research in machine learning, especially any original contributions you've made. We want to see how your work has pushed boundaries and had a real impact in production.
Be Specific About Your Skills:When listing your skills, be specific about your experience with generative models and the ML stack. Mention any hands-on experience you have with tools like PyTorch or JAX, as well as your knowledge of data pipelines.
Tailor Your Application:Don’t just send a generic application! Tailor it to our job description by using similar language and focusing on the skills and experiences that align with what we’re looking for. It shows us you’re genuinely interested.
Include 'Salmon':Remember to include the word 'Salmon' somewhere in your application. It’s a fun way for us to know you’ve read the job advert thoroughly. Plus, it shows you can follow instructions!
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 role is all about bridging the gap between research and real-world applications.
✨Know Your Tech Stack
Familiarise yourself with the modern ML stack, particularly Python frameworks like 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.
✨Demonstrate Project Management Skills
This role requires managing research projects from start to finish. Prepare examples of how you've formulated problems, evaluated solutions, and deployed systems in the past. This will show that you can handle the end-to-end process effectively.
✨Be Ready for a Fast-Paced Environment
If you have experience in a high-pace startup environment, share those stories! They want someone who can thrive under pressure and adapt quickly. Also, don’t forget to mention 'Salmon' somewhere in your application to show you’ve read the job advert thoroughly!
