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
- Company equity % in an early-stage startup.
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