AI Research Engineer – Kernel & Inference Optimization

Tether.to

. Drive innovation in model serving and inference architectures for advanced AI systems.

Posted 5/19/2026full-timeRemote • 🏈 Anywhere in North AmericaMid-LevelSeniorWebsite

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

Health insurance
Flexible working hours
Paid time off
Professional development opportunities

ATS Keywords

✓ Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

model serving architecturesinference optimizationMetal Shading Language (MSL)low-level kernel optimizationsGPU kernelsend-to-end inference pipelineslatency optimizationevaluation frameworksTensor ParallelismPipeline Parallelism

Soft Skills

innovationproblem-solvingempirical research applicationperformance metrics establishmentcollaboration

Certifications

PhD in NLPPhD in Machine Learning