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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 a competitive salary, equity options, and comprehensive dental and vision insurance.
- Why this job: Be part of a dynamic startup, tackling exciting challenges in AI and machine learning.
- Qualifications: Strong software engineering skills and a solid foundation in machine learning principles required.
- Other info: Work alongside industry experts from top tech companies and contribute to innovative projects.
The predicted salary is between 64000 - 96000 £ per year.
Who We Are
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.
Team you will work with
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
We are hiring a Machine Learning engineer. 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
- £80-150k base.
- Company equity % in an early-stage startup.
- 100% company-paid private Dental & Vision insurance.
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. This will not only help you understand the role 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.
✨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 impress the interviewers.
✨Tip Number 4
Network with professionals in the AI and machine learning community, especially those who have experience in video security. Engaging with industry experts can provide valuable insights and potentially lead to referrals for the position.
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, particularly with Pytorch and C++. Emphasise any projects where you've fine-tuned models or worked on production-grade software.
Craft a Strong Cover Letter: In your cover letter, express your passion for AI and video security. Mention specific projects or research that align with Coram.AI's goals and how your skills can contribute to their mission.
Showcase Your Technical Skills: Include a section in your application that details your technical skills, especially in machine learning fundamentals, deep learning research, and any experience with Docker or containerization.
Demonstrate Problem-Solving Ability: Provide examples of how you've solved complex problems in previous roles, particularly those related to latency and accuracy trade-offs in machine learning applications. This will show your fit for the role.
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 optimised them for performance, as this is crucial for the role.
✨Understand the Company’s Vision
Research Coram.AI and their approach to AI video systems. Understanding their mission and how they differentiate themselves in the market will help you align your answers with their goals during the interview.
✨Prepare for Problem-Solving Questions
Expect questions that assess your ability to design experiments and evaluate trade-offs between latency and accuracy. Practise explaining your thought process clearly, as this will demonstrate your analytical skills.
✨Stay Updated on Industry Trends
Familiarise yourself with the latest developments in deep learning and video security applications. Being able to discuss recent research papers or advancements will show your passion and commitment to the field.
