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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, remote work options, and a dynamic startup environment.
- Why this job: Work on exciting challenges at the forefront of AI and video technology in a fast-paced team.
- Qualifications: Strong software engineering skills and a solid foundation in machine learning are essential.
- Other info: Collaborate with 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 hiring a Machine Learning engineer to:
- Take an existing open-source Pytorch model, fine-tune, productionize them in C++ runtime, and optimize for latency and throughput.
- Take an open-source model and fine-tune them on our in-house data set as needed.
- Design thoughtful experiments in evaluating the tradeoffs between latency and accuracy on the end customer use case.
- Integrate the model with the downstream use case and fully own the end metrics.
- Maintain and improve all existing ML applications in the product.
- Read research papers and develop ideas on how they could be applied to video security use cases, and convert 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 optimizing 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) employer: Coram AI
Contact Detail:
Coram AI Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer (UK)
✨Tip Number 1
Familiarise yourself with the latest advancements in machine learning, particularly in video AI and deep learning. Follow relevant research papers and industry news to discuss these topics during your interview, showcasing your passion and knowledge.
✨Tip Number 2
Demonstrate your coding skills by working on personal projects or contributing to open-source projects that involve Pytorch and C++. This hands-on experience will not only enhance your skills but also provide you with concrete examples to discuss in interviews.
✨Tip Number 3
Prepare to discuss how you would approach optimising machine learning models for latency and throughput. Think of specific scenarios where you’ve tackled similar challenges, as this will show your problem-solving abilities and technical expertise.
✨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 insights into the role and may even lead to referrals, increasing your chances of landing the job.
We think you need these skills to ace Machine Learning Engineer (UK)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning, software engineering, and any specific technologies mentioned in the job description, such as Pytorch, C++, and Docker.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for AI and video security. Mention specific projects or experiences that demonstrate your skills in fine-tuning models and optimising performance.
Showcase Your Technical Skills: In your application, include examples of your work with machine learning models, particularly any experience with productionising Pytorch models or working with latency and throughput optimisation.
Demonstrate Continuous Learning: Mention any recent research papers you've read or relevant courses you've taken to show that you stay updated with the latest developments in machine learning and deep learning.
How to prepare for a job interview at Coram AI
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Pytorch, C++, and any relevant machine learning projects. Bring examples of your work that demonstrate your ability to fine-tune models and optimise for latency and throughput.
✨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 unprecedented insights and improving user experience.
✨Prepare for Problem-Solving Questions
Expect to tackle questions that assess your problem-solving abilities, especially around trade-offs between latency and accuracy. Think through potential scenarios and how you would approach them.
✨Stay Updated on Industry Trends
Familiarise yourself with the latest developments in deep learning and AI, particularly in relation to video security. Being able to discuss recent research papers or innovations will show your passion and commitment to the field.
