Remote AI Systems Engineer: Kernel & Inference Optimization

Jobgether SRL; Abu Dhabi; Remote; AED 250,000 - 360,000; Full time

Benefits offered by this job

Remote-first environment

International team

Exposure to cutting-edge AI research

Job summary

Jobgether SRL is seeking an AI Research Engineer (Kernel & Inference Optimization) in the United Arab Emirates to advance model serving for edge and mobile platforms. You will bridge AI research with systems engineering to optimize latency, throughput, and memory usage across diverse hardware.

Responsibilities include designing high-throughput inference pipelines, benchmarking, and developing custom GPU kernels. A PhD in NLP/ML or CS degree with strong research output is highly valued.

Qualifications

• Degree in Computer Science or a related technical field; a PhD in NLP, Machine Learning, or a related discipline is highly relevant, particularly with a strong AI research track record and publications at leading conferences.
• Proven expertise in Metal Shading Language (MSL), including the ability to write custom compute shaders from scratch.
• Demonstrated experience with low-level kernel optimization and inference optimization on mobile or other resource-constrained devices.
• Track record of delivering measurable improvements in inference latency, throughput, and memory footprint for domain-specific applications.
• Deep understanding of modern model-serving architectures, inference engines, and optimization techniques for high-performance AI deployment.
• Strong experience writing GPU kernels for mobile devices.

Responsibilities

• Design and deploy advanced model-serving architectures optimized for high throughput, low latency, and efficient memory utilization.
• Develop inference pipelines capable of operating effectively across diverse environments, including resource-constrained mobile devices and edge platforms.
• Establish clear performance targets covering response latency, token generation speed, throughput, memory footprint, and reliability.
• Build and execute controlled inference benchmarks in simulated and production environments, tracking latency, throughput, memory consumption, and error rates.
• Create and maintain representative datasets and simulation scenarios for evaluating model performance under real-world and resource-constrained conditions.
• Identify computational and memory bottlenecks across inference pipelines and implement solutions involving batching, networking, memory management, and other system-level optimizations.
• Develop custom GPU kernels and compute shaders for mobile hardware, including solutions written in Metal Shading Language (MSL).
• Apply advanced inference optimization techniques such as pruning, quantization, Flash Attention, KV caching, and speculative decoding.
• Design and optimize distributed inference systems using approaches such as tensor parallelism, pipeline parallelism, and expert parallelism for large-scale GPU workloads.
• Work with cross-functional engineering and research teams to integrate optimized inference frameworks into production and edge-device applications.
• Define evaluation methodologies, document experimental results, compare performance against established benchmarks, and continuously refine optimization strategies.
• Monitor production performance and use empirical research to identify opportunities for further improvements in scalability, efficiency, and reliability.

Skills

MSL

Low-level kernel optimization

Inference optimization

GPU kernels for mobile

Model serving architectures

Diffusion models

Vision Transformers

Empirical benchmarking

Distributed inference

English communication

Education

PhD in NLP/ML

Degree in Computer Science

Tools

MSL

Mobile GPU programming

Job description

Jobgether SRL is seeking an AI Research Engineer (Kernel & Inference Optimization) in the United Arab Emirates to advance model serving for edge and mobile platforms. You will bridge AI research with systems engineering to optimize latency, throughput, and memory usage across diverse hardware.

Responsibilities include designing high-throughput inference pipelines, benchmarking, and developing custom GPU kernels. A PhD in NLP/ML or CS degree with strong research output is highly valued.