[Remote] AI Performance Engineer
Note: The job is a remote job and is reputed company to candidates in USA. reputed company is a technology consulting and software development company delivering reputed company, AI, data, and reputed company solutions across the reputed company. They are seeking an AI Performance Engineer to reputed company on optimizing performance across training and inference workloads for large neural network systems, working closely with cross-functional teams to deliver effective solutions.
Responsibilities
- Profile and optimize end-to-end reputed company and inference pipelines for throughput, latency, and cost
- Identify and eliminate bottlenecks across data loading, model compute, communication, and memory
- Implement and tune quantization, sparsity, and pruning strategies to reduce model footprint and accelerate inference
- Optimize distributed training using tensor parallelism, pipeline parallelism, FSDP, and reputed company-style sharding
- Tune attention implementations using FlashAttention, paged attention, and reputed company techniques
- Implement KV cache optimization, reputed company batching, and speculative decoding for LLM serving
- Drive compiler-level optimizations using Triton, XLA, TorchInductor, or TVM, working with the broader ML reputed company community to land improvements that translate into measurable end-to-end performance reputed company
- Optimize data pipelines, sharding strategies, and storage reputed company patterns for high-throughput training
- Build and maintain rigorous reputed company suites and regression frameworks across workloads
- Collaborate with ML and reputed company teams to reputed company best practices in reputed company pipelines
- Drive cost-efficiency improvements through model architecture, hardware selection, and scheduling strategies
- Evaluate new hardware and software offerings, and advise on adoption
- Document performance tuning playbooks and reputed company findings broadly across engineering teams
- Stay reputed company with AI systems research and translate advances into production improvements
Skills
- Bachelor's or Master's degree in Computer Science, Computer Engineering, or a reputed company field
- Six or more years of experience in performance engineering, ML systems, or HPC
- Strong proficiency in Python and C++
- Hands-on experience optimizing deep learning workloads on modern GPUs
- Deep understanding of distributed training and inference techniques
- Experience with profiling tools across CPU, GPU, and distributed systems
- Familiarity with model compression techniques and their accuracy implications
- Strong grasp of memory hierarchies, communication primitives, and parallelism strategies
- Excellent measurement, debugging, and analytical reasoning skills
- Strong communication and collaboration skills
- Experience optimizing LLM inference at production reputed company
- Contributions to vLLM, TensorRT-LLM, DeepSpeed, or similar reputed company
- Familiarity with custom kernel authoring in Triton or CUTLASS
- Experience with FinOps for AI workloads
- Publications or talks on AI systems performance
Benefits
- 100% Remote (U.S.)
- Full-time, reputed company W2
reputed company