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- Tasks: Join us to fine-tune and productionise cutting-edge AI models for video security.
- Company: Be part of a well-funded tech startup revolutionising the video security industry.
- Benefits: Enjoy a dynamic work environment with opportunities for growth and innovation.
- Why this job: Make a real impact by optimising AI for better user experiences in video security.
- Qualifications: Strong software engineering skills and a solid foundation in machine learning required.
- Other info: Stay ahead by engaging with the latest deep learning research and technologies.
The predicted salary is between 43200 - 72000 £ per year.
We are working with a company who is building the best business AI video system on the market. Powered by the next-generation video artificial intelligence, they deliver unprecedented insights and 10x better user experience than the incumbents of the vast but stagnant video security industry. The company is a tech start-up which is well funded and has been going for 3 years now.
The role involves hiring a Machine Learning engineer to:
- Take an existing open-source Pytorch model, fine-tune, productionise them in C++ runtime, and optimise 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 trade-offs 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 productionising a Pytorch model developed in C++, profiling the model for latency, finding bottlenecks, and optimising them (Good to have).
- 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 (Good to have).
Senior Machine Learning Engineer - Pytorch & C++ employer: LinkedIn
Contact Detail:
LinkedIn Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Machine Learning Engineer - Pytorch & C++
✨Tip Number 1
Familiarise yourself with the latest advancements in machine learning, particularly in Pytorch and C++. Follow relevant research papers and online courses to deepen your understanding. This will not only enhance your knowledge but also demonstrate your commitment to staying updated in the field.
✨Tip Number 2
Engage with the community by participating in forums or contributing to open-source projects related to video AI systems. This can help you build a network of professionals in the industry and showcase your skills in practical applications.
✨Tip Number 3
Prepare to discuss specific projects where you've optimised models for latency and throughput. Be ready to share your thought process on evaluating trade-offs between accuracy and performance, as this is crucial for the role.
✨Tip Number 4
Showcase your ability to integrate machine learning models into real-world applications. Think of examples where you've taken a model from development to production, and be prepared to discuss the challenges you faced and how you overcame them.
We think you need these skills to ace Senior Machine Learning Engineer - Pytorch & C++
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 Strong Cover Letter: In your cover letter, express your passion for AI and video security. Mention specific projects or research that align with the company's goals and how your skills can contribute to their success.
Showcase Your Technical Skills: Include a section in your application that details your technical skills, especially in machine learning fundamentals, productionising models, and familiarity with tools like Docker and ONNX runtime.
Demonstrate Continuous Learning: Mention any recent research papers you've read or courses you've taken related to deep learning and AI. This shows your commitment to staying updated in the field and your ability to apply new knowledge.
How to prepare for a job interview at LinkedIn
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Pytorch and C++. Highlight specific projects where you've fine-tuned models or optimised performance. Demonstrating your technical prowess will be crucial for this role.
✨Understand the Company’s Vision
Research the company’s AI video system and its impact on the video security industry. Being able to articulate how your skills align with their mission will show your genuine interest in the position.
✨Prepare for Problem-Solving Questions
Expect questions that assess your ability to evaluate trade-offs between latency and accuracy. Think of examples from your past work where you had to make similar decisions and be ready to explain your thought process.
✨Stay Updated on Latest Research
Familiarise yourself with recent advancements in deep learning and video security applications. Mentioning relevant research papers during the interview can demonstrate your commitment to staying at the forefront of the field.
