AI/ML Systems Performance Engineer — Remote + Equity

Lightningai; Greater London; Hybrid; GBP 89,000 - 185,000; Full time

Benefits offered by this job

Health coverage

Equity

Retirement plan

Unlimited PTO

Hybrid work

Office meals

Sabbatical

Job summary

Lightning AI, the company behind PyTorch Lightning, is hiring a Research Engineer to optimize training and inference workloads on Lightning AI infrastructure. The role sits at the intersection of ML systems, AI infrastructure, performance engineering, and practical research, spanning models, inference systems, and platform infrastructure.

You’ll collaborate with customers and engineers to improve scalability, reliability, and performance of real-world AI workloads, across GPUs, accelerators, and

Qualifications

• Strong experience with PyTorch and large-scale training/inference.
• Familiarity with distributed systems and parallelism strategies.
• Strong software engineering fundamentals and production-quality code.
• Experience analyzing performance bottlenecks in ML systems or distributed workloads.
• Excellent collaboration and communication skills across teams and customers.
• Bachelor’s degree in Computer Science, Engineering, or related field.

Responsibilities

• Optimize large-scale training and inference workloads across GPUs and distributed systems.
• Analyze workloads with customers to identify bottlenecks and improve performance.
• Develop inference pipelines, model serving, and performance-oriented tooling.
• Design profiling, debugging, and observability tools for model execution.
• Ensure performance improvements are accessible via clean APIs and tooling.

Skills

PyTorch

Large-scale training

Distributed systems

API design

Production-grade code

Cross-functional collaboration

Ambiguity tolerance

ML systems

Education

Bachelor’s degree in Computer Science, Engineering, or related field

Tools

CUDA

Triton

TensorRT

vLLM

SGLang

Dynamo

Job description

Lightning AI, the company behind PyTorch Lightning, is hiring a Research Engineer to optimize training and inference workloads on Lightning AI infrastructure. The role sits at the intersection of ML systems, AI infrastructure, performance engineering, and practical research, spanning models, inference systems, and platform infrastructure.

You’ll collaborate with customers and engineers to improve scalability, reliability, and performance of real-world AI workloads, across GPUs, accelerators, and