Requirements:

• 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 such as smartphones.
• Practical experience developing and deploying end-to-end inference pipelines, from model optimization through production integration on constrained hardware.
• Strong ability to apply empirical research and systematic experimentation to solve latency, computational, and memory challenges.
• Experience designing robust evaluation and benchmarking frameworks for inference systems.
• Knowledge of distributed inference techniques, including tensor parallelism, pipeline parallelism, and expert parallelism for large-scale GPU clusters.
• Deep understanding of the mathematical foundations and architecture of diffusion models and Vision Transformers.
• Familiarity with modern inference optimization techniques including pruning, quantization, Flash Attention, KV Cache optimization, and speculative decoding such as EAGLE.
• Strong analytical and problem-solving abilities, with an ability to investigate complex system bottlenecks and turn research findings into practical engineering solutions.
• Excellent English communication skills and the ability to collaborate effectively with distributed, cross-functional technical teams.

Benefits:

• Opportunity to work on advanced AI systems spanning model serving, inference optimization, mobile computing, edge deployment, and large-scale distributed inference.
• Remote-first working environment with an international team.
• Exposure to cutting-edge AI research and practical systems engineering challenges.
• Opportunity to contribute to performance-critical infrastructure where improvements can have a measurable impact on real-world AI applications.
• Collaborative environment combining research-driven experimentation with hands-on engineering.
• Opportunity to work with advanced model architectures including diffusion models, Vision Transformers, and multimodal systems.
• Access to challenging technical problems involving GPU kernels, inference engines, memory optimization, and distributed computing.

How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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