AI Research Engineer – Kernel & Inference Optimization
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Job Description
AI Research Engineer responsible for optimizing model serving and inference architectures. Join Tether to innovate in the fintech space remotely from India.
Responsibilities:
- Design and deploy state-of-the-art model serving architectures that deliver high throughput and low latency while optimizing memory usage
- Ensure these pipelines run efficiently across diverse environments
- Establish clear performance targets such as reduced latency, improved token response, and minimized memory footprint
- Build, run, and monitor controlled inference tests in both simulated and live production environments
- Track key performance indicators such as response latency, throughput, memory consumption, and error rates
- Document iterative results and compare outcomes against established benchmarks
- Identify and prepare high-quality test datasets and simulation scenarios
- Analyze computational efficiency and diagnose bottlenecks in the serving pipeline
- Work closely with cross-functional teams to integrate optimized serving and inference frameworks into production pipelines
Requirements:
- A degree in Computer Science or related field
- Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D (with good publications in A* conferences)
- Must have knowledge of Metal Shading Language (MSL)
- Proven experience in low-level kernel optimizations and inference optimization on mobile devices is essential
- A deep understanding of modern model serving architectures and inference optimization techniques
- Must have strong expertise in writing GPU kernels for mobile devices (i.e., smartphones)
- Practical experience in developing and deploying end-to-end inference pipelines
- Demonstrated ability to apply empirical research to overcome challenges in model serving
- Distributed Inference Systems: Designing and optimizing high-performance inference engines
Benefits:
- Professional development opportunities
- Working remotely from every corner of the world
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