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- Tasks: Deploy and enhance machine learning models in real-world energy and industrial settings.
- Company: Exciting deep tech startup focused on AI and energy efficiency.
- Benefits: Remote work, competitive salary, and the chance to make a global impact.
- Why this job: Be at the forefront of AI technology, transforming research into real-world applications.
- Qualifications: Proficient in Python, deep learning frameworks, and experience with productionising ML models.
- Other info: Join a well-funded startup with excellent growth opportunities and support for your career journey.
The predicted salary is between 36000 - 60000 £ per year.
You will deploy and harden machine learning components in real-world energy and industrial environments. This role bridges the gap between research and production by operationalising physics-informed and deep learning models under real-world constraints. You will ensure the robustness, stability, and explainability of AI systems within complex physical infrastructure and field environments.
Location: Remote
Why this role is remarkable:
- Deploy cutting-edge physics-informed ML models that have a direct impact on global energy and industrial efficiency.
- Work at a well-funded startup backed by top-tier VCs, operating at the intersection of deep learning and physical science.
- Lead the transition from lab-based research to production-grade deployments in complex, high-stakes operational environments.
What you will do:
- Own and deploy end-to-end ML pipelines including data validation, feature extraction, model serving, and performance monitoring.
- Adapt foundation models and algorithms to site-specific industrial data and infrastructure constraints.
- Collaborate with research teams to translate novel physics-informed methods into production-grade, reliable systems.
The ideal candidate:
- Proficient in Python and deep learning frameworks like PyTorch or JAX, with experience in Docker and Kubernetes.
- Strong background in applied ML for time series or operational AI within industrial or energy contexts.
- Proven experience productionising ML models with a focus on CI/CD integration, observability, and system reliability.
Machine Learning Engineer at deep tech industrial AI startup in London employer: Jack & Jill/External Ats
Contact Detail:
Jack & Jill/External Ats Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer at deep tech industrial AI startup in London
✨Tip Number 1
Get to know the company inside out! Research their projects, values, and culture. This will help you tailor your conversations and show that you're genuinely interested in what they do.
✨Tip Number 2
Practice makes perfect! Run through common interview questions related to machine learning and your technical skills. The more comfortable you are with your answers, the more confident you'll feel during the actual interview.
✨Tip Number 3
Network like a pro! Connect with current employees on LinkedIn or attend industry events. Building relationships can give you insider info and even a foot in the door for opportunities.
✨Tip Number 4
Don’t forget to apply through our website! Talking to Jack is a game-changer. He’ll help you highlight your strengths and ensure Jill considers you for this role. Plus, it’s all free!
We think you need these skills to ace Machine Learning Engineer at deep tech industrial AI startup in London
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter for the Machine Learning Engineer role. Highlight your experience with Python, deep learning frameworks, and any relevant projects that showcase your skills in deploying ML models in industrial settings.
Showcase Your Impact: When detailing your past experiences, focus on the impact you've made. Use metrics and examples to illustrate how your work has improved efficiency or stability in previous roles, especially in energy or industrial contexts.
Be Clear and Concise: Keep your application clear and to the point. Avoid jargon unless it's necessary, and make sure your passion for machine learning and its applications shines through. We want to see your personality as well as your qualifications!
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to connect you with Jack and Jill, who can help you navigate the process and ensure your application gets the attention it deserves.
How to prepare for a job interview at Jack & Jill/External Ats
✨Know Your Tech Inside Out
Make sure you’re well-versed in Python and the deep learning frameworks mentioned, like PyTorch or JAX. Brush up on your knowledge of Docker and Kubernetes too, as these are crucial for deploying ML models in real-world environments.
✨Showcase Real-World Applications
Prepare to discuss how you've applied machine learning in industrial or energy contexts. Be ready to share specific examples of how you've productionised ML models, focusing on CI/CD integration and system reliability.
✨Understand the Company’s Mission
Research the startup's focus on energy efficiency and industrial AI. Be prepared to explain how your skills can contribute to their goals, especially in deploying physics-informed ML models that make a tangible impact.
✨Ask Insightful Questions
During the interview, ask questions that show your interest in the role and the company. Inquire about their current projects, challenges they face in deploying ML models, and how they measure success in their deployments.
