You will be part of a high-performance platform engineering team focused on optimizing and deploying advanced neural networks on a next-generation compute architecture. This role sits at the intersection of machine learning, systems, and algorithm design, with a strong focus on extracting maximum performance from hardware.
You will be responsible for:
Driving the lowering and optimization of cutting-edge deep neural networks on a specialised compute platform
Applying mathematical and algorithmic optimisation techniques to solve complex, large-scale problems
Designing heuristics and optimisation strategies for computationally intensive and NP-hard challenges
Collaborating with software teams to optimise graph-based execution and performance
Continuously improving performance through deep understanding of hardware-software interaction

Ideal Candidate
You have an MS or PhD in Computer Science or a related field with 8+ years of industry experience
You have a solid background in numerical and/or algorithmic optimisation




You have experience designing heuristics for complex or NP-hard problems
You have knowledge of both classical and machine learning algorithms (e.g. Computer Vision, DSP, Deep Learning)
You have a robust foundation in graph algorithms and related data structures
You are comfortable working at the intersection of systems, performance, and machine learning

Nice to Have
You have proficiency in C++ (C++11 or later)
You have experience with frameworks such as TVM or MLIR
You have knowledge of front-end and back-end compiler techniques

What’s on Offer
Competitive compensation with meaningful equity
High-impact role in a deeply technical, low-bureaucracy setting
Opportunity to work on cutting-edge compute systems and long-term career growth

About the employer
Our client is a Silicon Valley–based deep-tech company building a current compute architecture for real-time AI at the edge. Founded by engineers from leading researc

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