AI Research Engineer innovating post - training methodologies at Tether for agentic behavior and tool use optimization. Engaging in cutting - edge AI research on large language models and decision - making.
About the role
- AI Research Engineer specializing in kernel and inference optimization for advanced AI systems at Tether. Driving innovation in model serving architectures with a global remote team.
Responsibilities
- Drive innovation in model serving and inference architectures for advanced AI systems.
- Focus on optimizing model deployment and inference strategies.
- Work on a wide spectrum of systems, from resource-efficient models to complex, multi-modal architectures.
- Develop, test, and implement novel serving strategies and inference algorithms.
- Engineer robust inference pipelines, establish performance metrics, and resolve bottlenecks in production environments.
- Enable high-throughput, low-latency, low-memory footprint, and scalable AI performance that delivers tangible value.
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.
- Your contributions should have led to measurable improvements in inference latency, throughput, and memory footprint for domain-specific applications, particularly on resource-constrained devices and edge platforms.
- A deep understanding of modern model serving architectures and inference optimization techniques is required.
- Strong expertise in writing GPU kernels for mobile devices (i.e., smartphones).
- Practical experience in developing and deploying end-to-end inference pipelines, from optimizing models for efficient serving to integrating these solutions on resource-constrained devices is required.
- Demonstrated ability to apply empirical research to overcome challenges in model serving, such as latency optimization, computational bottlenecks, and memory constraints.
- Proficient in designing robust evaluation frameworks and iterating on optimization strategies to continuously push the boundaries of inference performance and system efficiency.
- Distributed Inference Systems: Designing and optimizing high-performance inference engines using techniques like Tensor Parallelism, Pipeline Parallelism, and Expert Parallelism to handle massive models on GPU clusters.
- Deep understanding of the math and structure behind Diffusion Models and Vision Transformers.
Benefits
- Health insurance
- Flexible working hours
- Paid time off
- Professional development opportunities
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