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 strong 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 strong 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 environment
- Opportunity to work on cutting-edge compute systems and long-term growth opportunities
About the employer
Our client is a Silicon Valley–based deep-tech company building a new compute architecture for real-time AI at the edge. Founded by engineers from leading researc
📌 Deep Learning Compiler Engineer (Pune)
🏢 a Snaphunt Client
📍 Pune
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