Machine Learning Engineer - Model Optimization

Description du poste

We are seeking experienced ML engineers to optimize and deploy machine learning models on heterogeneous embedded computing platforms. You will work at the intersection of machine learning, compilers, runtime systems, and computer architecture, helping bridge the gap between models developed by researchers and highly optimized implementations running on production hardware.

A major focus of this role is understanding the trade-offs between model quality and computational efficiency. You will work closely with machine learning researchers to develop and evaluate hardware-aware model architectures, identify computational bottlenecks, and explore architectural changes that improve latency, throughput and memory usage while maintaining model quality.

The ideal candidate enjoys understanding both neural network architectures and the hardware on which they execute, and is interested in techniques such as hardware-aware neural architecture search, model scaling, quantization, mixed-precision inference, and model compression.

It is an exciting opportunity to tackle real-world challenges in bringing algorithms developed in the lab to vehicles operating in diverse physical environments.

About Zendar

Zendar builds a radar-centric autonomy stack which makes any vehicle - from cars to robots - autonomous in any environment. With our deep radar DNA, we have architected our solution to put RF sensing at the core of all perception. The result is a system that handles long range, high speeds, and bad weather not as edge cases but as a core strength of the autonomy stack.

Because radars naturally measure both 3D position and velocity for every object in the environment, radar-centric autonomy is extremely compute- and data-efficient. Our autonomous vehicle needs only a few thousand dollars of hardware to make it completely autonomous, making this the cheapest way to build an autonomous vehicle by far.

See a demo of Zendar's foundational RF perceptio

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