Machine Learning Engineer (UK)...

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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