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- Tasks: Join us as a Machine Learning Engineer to optimise AI video systems and enhance user experience.
- Company: Coram.AI is revolutionising the video security industry with cutting-edge AI technology.
- Benefits: Enjoy company equity, a dynamic startup environment, and opportunities for rapid personal growth.
- Why this job: Be part of a passionate team solving real-world problems in AI and user experience.
- Qualifications: Strong software engineering skills and a solid foundation in machine learning are essential.
- Other info: Work alongside industry experts from top tech companies and contribute to innovative projects.
The predicted salary is between 36000 - 60000 £ per year.
Started in 2021, Coram.AI is building the best business AI video system on the market. Powered by the next-generation video artificial intelligence, we deliver unprecedented insights and 10x better user experience than the incumbents of the vast but stagnant video security industry. Our customers range from warehouses, schools, hospitals, hotels, and many more, and we are growing rapidly. We are looking for someone to join our team to help us scale our systems to meet the user demand and to ship new features.
Founded by Ashesh (CEO) and Peter (CTO), we are serial entrepreneurs and experts in AI and robotics. Our engineering team is composed of industry experts with decades of research and experience from Lyft, Google, Zoox, Toyota, Facebook, Microsoft, Stanford, Oxford, and Cornell. Our go-to-market team consists of experienced leaders from Verkada. We are venture-backed by 8VC + Mosaic, revenue-generating, and have multiple years of runway.
Being part of our team means solving interesting problems at the intersection of user experience, machine learning and infrastructure. It also means committing to excellence, learning, and delivering great products to our customers in a high-velocity startup.
The role involves:
- Taking an existing open-source Pytorch model, fine-tuning, productionizing them in C++ runtime, and optimising for latency and throughput.
- Fine-tuning an open-source model on our in-house data set as needed.
- Designing thoughtful experiments in evaluating the trade-offs between latency and accuracy on the end customer use case.
- Integrating the model with the downstream use case and fully owning the end metrics.
- Maintaining and improving all existing ML applications in the product.
- Reading research papers and developing ideas on how they could be applied to video security use cases, and converting those ideas to working code.
Requirements:
- You should be a good software engineer who enjoys writing production-grade software.
- Strong machine learning fundamentals (linear algebra, probability and statistics, supervised and self-supervised learning).
- Keeping up with the latest in deep learning research, reading research papers, and familiarity with the latest developments in foundation models and LLMs.
- (Good to have) Comfortable with productionizing a Pytorch model developed in C++, profiling the model for latency, finding bottlenecks, and optimising them.
- Good understanding of docker and containerization.
- (Good to have) Experience with Pytorch and Python3, and comfortable with C++.
- (Good to have) Understanding of Torch script, ONNX runtime, TensorRT.
- (Good to have) Understanding of half-precision inference and int8 quantization.
What we offer:
- Company equity % in an early-stage startup.
Machine Learning Engineer (UK) (London) employer: Coram AI
Contact Detail:
Coram AI Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer (UK) (London)
✨Tip Number 1
Familiarise yourself with the latest advancements in machine learning, particularly in video AI and deep learning. This will not only help you understand the company's products better but also allow you to engage in meaningful conversations during interviews.
✨Tip Number 2
Showcase your hands-on experience with Pytorch and C++ by working on personal projects or contributing to open-source initiatives. This practical experience can set you apart from other candidates and demonstrate your ability to productionise models effectively.
✨Tip Number 3
Prepare to discuss specific examples of how you've optimised machine learning models for latency and throughput. Being able to articulate your thought process and the results of your experiments will highlight your problem-solving skills.
✨Tip Number 4
Network with professionals in the AI and machine learning community, especially those who have experience in video security applications. Engaging with industry experts can provide valuable insights and potentially lead to referrals within the company.
We think you need these skills to ace Machine Learning Engineer (UK) (London)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning, software engineering, and any specific projects involving Pytorch or C++. Emphasise your understanding of deep learning fundamentals and any practical applications you've worked on.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Mention specific projects or experiences that align with the job description, such as fine-tuning models or optimising performance. Show that you understand the challenges in the video security industry.
Showcase Your Technical Skills: If applicable, include links to your GitHub or portfolio showcasing relevant projects. Highlight any contributions to open-source projects, especially those related to machine learning or video processing, to demonstrate your hands-on experience.
Prepare for Technical Questions: Anticipate technical questions related to machine learning concepts, model optimisation, and programming in C++. Brush up on your knowledge of linear algebra, probability, and statistics, as well as recent advancements in deep learning research.
How to prepare for a job interview at Coram AI
✨Showcase Your Technical Skills
Be prepared to discuss your experience with machine learning frameworks, particularly Pytorch. Highlight any projects where you've fine-tuned models or worked with C++, as this is crucial for the role.
✨Understand the Company’s Vision
Research Coram.AI and its mission in the video security industry. Be ready to explain how your skills can contribute to their goal of delivering better user experiences and insights through AI.
✨Prepare for Problem-Solving Questions
Expect technical questions that assess your problem-solving abilities. Practice explaining your thought process when designing experiments or optimising models for latency and accuracy.
✨Demonstrate Continuous Learning
Show your passion for staying updated with the latest developments in machine learning. Discuss recent research papers you've read and how you envision applying those concepts to real-world problems in video security.
