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: Flexible remote work, competitive salary, and opportunities for personal and professional growth.
- Other info: Work in a fast-paced environment with a focus on continuous improvement and career development.
- 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.
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.
Remote Machine Learning Engineer in London employer: Generative Engineering
Generative Engineering is an exceptional employer for a Remote Machine Learning Engineer, offering a unique blend of early-stage start-up agility and the stability of a well-established team. With a strong focus on employee growth, we provide opportunities to work on cutting-edge generative models while contributing to impactful systems that engineers rely on daily. Our collaborative work culture encourages innovation and continuous improvement, ensuring that as our team members grow, so does our pioneering product.
StudySmarter Expert Advice🤫
We think this is how you could land Remote Machine Learning Engineer 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 projects, especially those that highlight your experience with generative models and real-world ML systems. This will make you stand out when chatting with potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice explaining your past research and production impacts clearly and confidently—this is your chance to shine!
✨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 genuinely interested in joining our mission to revolutionise engineering design.
We think you need these skills to ace Remote Machine Learning Engineer 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 impacted production systems, so don't hold back on the details!
Tailor Your Application:When applying, tailor your CV and cover letter to reflect the skills and experiences mentioned in the job description. We love seeing candidates who understand our needs and can demonstrate how they fit into our vision.
Include 'Salmon':Just a little fun tip: remember to include the word 'Salmon' somewhere in your application. It’s a simple way for us to know you’ve read the job advert thoroughly!
Apply Through Our Website:We encourage you to apply through our website for a smoother process. It helps us keep track of applications and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at Generative Engineering
✨Showcase Your Research Impact
Make sure to highlight your past research contributions, especially those that have had a tangible impact in production. Be ready to discuss specific projects where your work has led to real-world applications, as this will resonate well with the team’s focus on generative models.
✨Demonstrate Technical Proficiency
Brush up on your knowledge of generative modelling techniques and frameworks like PyTorch or JAX. Be prepared to discuss the trade-offs of different architectures, such as transformers and diffusion models, and how you've applied them in your previous work.
✨Talk About Project Management Experience
Since the role involves managing research projects from start to finish, be ready to share examples of how you've formulated problems, evaluated solutions, and deployed systems. This will show that you can handle the end-to-end process effectively.
✨Familiarise Yourself with the Company Culture
Understand the dynamics of a high-paced startup environment and be ready to discuss how you can contribute to building data infrastructure. Mention any experience with open-source projects or collaborative efforts, as this aligns with the company’s ethos of continuous improvement.
