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For Companies At a Glance
- Tasks: Design and develop advanced deep learning models for real-time forecasting and decision intelligence.
- Company: Fast-growing AI-driven tech company modernising prediction systems for real-world industries.
- Benefits: Competitive salary, hybrid working, and opportunities for publication and presentation.
- Why this job: Make a real impact with your work in a dynamic, innovative environment.
- Qualifications: Extensive research background in deep learning and experience with time series analysis.
- Other info: Collaborative culture with excellent career growth and high-impact projects.
The predicted salary is between 80000 - 120000 £ per year.
About the Company
Join a fast-growing AI-driven technology company that’s modernising prediction and decision systems for complex, real-world industries. The organisation builds powerful AI platforms that help partners, particularly in travel and transportation, automate commercial decisions, optimise revenue and personalise customer experiences through deep learning-based forecasting and analytics. With an emphasis on bridging legacy infrastructure with cutting-edge data science, the company’s solutions provide real-time insights, dynamic pricing, and revenue optimising recommendations across large, intricate datasets. Nearly hundreds of global partners rely on this platform to make confident automated decisions informed by advanced forecasting and deep neural networks. Here your work won’t sit in a research silo; your models will directly influence sophisticated, live operational systems. The environment values intellectual curiosity, scientific rigour, and practical impact, offering opportunities to publish and present on advancements that truly matter.
Role Overview
We’re seeking a Deep Learning Engineer with an exceptional research pedigree, proven expertise in neural networks, and substantial industry experience with time series analysis and forecasting. You’ll empower product teams to push the frontier of AI-driven decision intelligence by developing models that power real-time forecasting, optimisation, and predictive insights on complex temporal data.
Key Responsibilities
- Design, develop, and deploy advanced deep learning architectures for time series forecasting, decision intelligence, and sequential prediction.
- Translate research innovations into robust, production-quality systems that operate at scale and influence commercial outcomes.
- Collaborate with cross-functional teams, from ML engineers to product leaders to integrate models into forecasting and optimisation pipelines.
- Conduct rigorous benchmarking and experimentation, applying best practices from academic research to real-world data challenges.
- Drive publications and presentations in top venues, representing both theoretical innovation and applied breakthroughs.
What You Bring
Essential:
- Extensive research background in deep learning - demonstrated through publications in top-tier journals and conferences (NeurIPS, ICML, ICLR, JMLR, etc.).
- Strong experience with neural network models applied to time series, dynamic forecasting, and complex sequential tasks.
- Industry experience implementing and refining forecasting systems in production.
- Proficiency in modern ML frameworks such as PyTorch, TensorFlow, or JAX.
- A track record of applying research results to real-world, high-impact problems.
Highly Valued:
- Experience with real-time prediction systems, probabilistic forecasting, and uncertainty quantification.
- Hands-on expertise with cloud infrastructure and ML-oriented deployment workflows.
- Demonstrated ability to collaborate across research, engineering, and product teams.
Based in Central London
Salary £100,000 - £150,000 + bonus (DEO)
Hybrid working
Deep Learning Engineer in London employer: Block MB
Contact Detail:
Block MB Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Deep Learning Engineer in London
✨Tip Number 1
Network like a pro! Get out there and connect with folks in the AI and deep learning space. Attend meetups, webinars, or conferences where you can chat with industry experts and potential employers. Remember, sometimes it’s not just what you know, but who you know!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your deep learning projects, especially those involving time series analysis and forecasting. Share your work on platforms like GitHub or even your own website to demonstrate your expertise and passion for the field.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Be ready to discuss your past projects and how they relate to real-world applications. Practise common interview questions and maybe even do mock interviews with friends or mentors.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Tailor your application to highlight your experience with neural networks and forecasting systems, and let us see how you can make an impact in our team!
We think you need these skills to ace Deep Learning Engineer in London
Some tips for your application 🫡
Show Off Your Research Skills: Make sure to highlight your research background in deep learning. We want to see those publications and any cool projects you've worked on that demonstrate your expertise in neural networks and time series analysis.
Tailor Your Application: Don’t just send a generic CV and cover letter! We love it when applicants tailor their materials to our job description. Mention specific experiences that relate to deep learning architectures and forecasting systems.
Be Clear and Concise: When writing your application, keep it clear and to the point. We appreciate well-structured applications that make it easy for us to see your qualifications and how you can contribute to our team.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at Block MB
✨Know Your Deep Learning Stuff
Make sure you brush up on your deep learning knowledge, especially around neural networks and time series analysis. Be ready to discuss your past projects and how you've applied these concepts in real-world scenarios.
✨Showcase Your Research Experience
Since the company values a strong research background, prepare to talk about your publications and any significant contributions you've made to the field. Highlight how your research can translate into practical applications for their AI-driven platforms.
✨Collaborate Like a Pro
This role involves working with cross-functional teams, so be prepared to discuss your experience collaborating with ML engineers and product leaders. Share examples of how you've integrated models into production systems and the impact it had on decision-making.
✨Be Ready for Technical Challenges
Expect some technical questions or challenges during the interview. Brush up on modern ML frameworks like PyTorch or TensorFlow, and be ready to demonstrate your problem-solving skills with real-time prediction systems or forecasting tasks.
