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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 health insurance.
- Why this job: Be part of a dynamic startup, tackling exciting challenges in machine learning and user experience.
- 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.
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, productionising them in C++ runtime, and optimising for latency and throughput.
- Taking an open-source model and fine-tuning them 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:
- Good software engineering skills with a focus on 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 productionising a Pytorch model developed in C++, profiling the model for latency, finding bottlenecks, and optimising them.
- Good understanding of Docker and containerisation (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 quantisation.
What we offer:
- £80-150k base.
- Company equity % in an early-stage startup.
- 100% company-paid private Dental & Vision insurance.
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 security. Follow relevant research papers and industry news to discuss these insights during your interview, showcasing your passion and knowledge.
✨Tip Number 2
Demonstrate your hands-on experience with Pytorch and C++ by working on personal projects or contributing to open-source initiatives. This practical experience will not only enhance your skills but also provide concrete examples to discuss in interviews.
✨Tip Number 3
Network with professionals in the AI and machine learning community, especially those who have experience in video security applications. Attend meetups, webinars, or conferences to make connections that could lead to referrals or insider information about the role.
✨Tip Number 4
Prepare to discuss specific trade-offs between latency and accuracy in machine learning models. Think of examples from your past work or studies where you had to make similar decisions, as this will demonstrate your critical thinking and problem-solving skills.
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, particularly with Pytorch and C++. Emphasise any projects where you've fine-tuned models or optimised for latency and throughput.
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 their needs, such as working with open-source models or your understanding of deep learning research.
Showcase Your Technical Skills: Include a section in your application that details your technical skills, especially those mentioned in the job description like Docker, ONNX runtime, and TensorRT. Provide examples of how you've used these technologies in past projects.
Demonstrate Continuous Learning: Mention any recent courses, certifications, or research papers you've engaged with that relate to machine learning and AI. This shows your commitment to staying updated in a rapidly evolving field.
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 their approach to AI video systems. Familiarise yourself with their products and the industries they serve, as this will help you align your answers with their goals during the interview.
✨Prepare for Problem-Solving Questions
Expect to tackle questions that assess your problem-solving skills, particularly around trade-offs between latency and accuracy. Think through how you would design experiments and evaluate outcomes in a real-world context.
✨Stay Updated on Industry Trends
Demonstrate your commitment to continuous learning by discussing recent research papers or developments in deep learning and AI. This shows your passion for the field and your ability to apply new ideas to practical use cases.
